Apparatus, systems and methods for event recognition based on a wireless signal

ABSTRACT

Apparatus, systems and methods for recognizing and classifying events in a venue based on a wireless signal are disclosed. In one example, a disclosed system comprises a first transmitter, a second transmitter, at least one first receiver, at least one second receiver, and an event recognition engine, in the venue. The first transmitter transmits a training wireless signal through a wireless multipath channel impacted by a known event in the venue in a training time period associated with the known event. Each first receiver receives asynchronously the training wireless signal, and obtains, asynchronously based on the training wireless signal, at least one time series of training channel information of the wireless multipath channel between the first receiver and the first transmitter. The second transmitter transmits a current wireless signal through the wireless multipath channel impacted by a current event in a current time period associated with the current event. Each second receiver receives asynchronously the current wireless signal, and obtains, asynchronously based on the current wireless signal, at least one time series of current channel information of the wireless multipath channel between the second receiver and the second transmitter. The event recognition engine trains a classifier based on the training channel information; and apples the classifier to: classify the current channel information and associate the current event with at least one of: a known event, an unknown event and another event.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application hereby claims priority to, and incorporates byreference the entirety of the disclosures of, each of the followingapplications:

-   -   (a) U.S. patent application Ser. No. 15/326,112, entitled        “WIRELESS POSITIONING SYSTEMS”, filed on Jan. 13, 2017,        -   (1) which is a national stage entry of PCT patent            application PCT/US2015/041037, entitled “WIRELESS            POSITIONING SYSETMS”, filed on Jul. 17, 2015, published as            WO 2016/011433A2 on Jan. 21, 2016,            -   a. which claims priority to U.S. Provisional patent                application 62/148,019, entitled “WIRELESS POSITIONING                SYSTEMS”, filed on Apr. 15, 2015,            -   b. which is a continuation-in-part of U.S. patent                application Ser. No. 14/605,611, entitled “WIRELESS                POSITIONING SYSTEMS”, filed on Jan. 26, 2015, published                as US2016/0018508A1 on Jan. 21, 2016,                -   1. which claims priority to U.S. Provisional patent                    application 62/025,795, entitled “TIME-REVERSAL                    POSITIONING SYSTEMS”, filed on Jul. 17, 2014, and                -   2. which claims priority to U.S. Provisional patent                    application 62/069,090, entitled “TIME-REVERSAL                    POSITIONING SYSTEMS”, filed on Oct. 27, 2014,    -   (b) U.S. patent application Ser. No. 15/584,052, entitled        “METHOD, SYSTEM, AND APPARATUS FOR WIRELESS POWER TRANSMISSION        BASED ON POWER WAVEFORMING”, filed on May 2, 2017,        -   (1) which claims priority to U.S. Provisional patent            application 62/331,278, entitled “USING VIRTUAL ANTENNAS FOR            POWER WAVEFORMING IN WIRELESS POWER TRANSMISSION SYSTEMS”,            filed on May 3, 2016,    -   (c) U.S. patent application Ser. No. 15/434,813, entitled        “METHODS, DEVICES, APPARATUS, AND SYSTEMS FOR MEDIUM ACCESS        CONTROL IN WIRELESS COMMUNICATION SYSTEMS UTILIZING SPATIAL        FOCUSING EFFECT”, filed on Feb. 16, 2017,        -   (1) which claims priority to U.S. Provisional patent            application 62/295,970, entitled “THE IMPACT OF SPATIAL            FOCUSING EFFECTS ON MEDIUM ACCESS CONTROL DESIGN FOR 5G”,            filed on Feb. 16, 2016,        -   (2) which claims priority to U.S. Provisional patent            application 62/320,965, entitled “OPTIMAL RATE ADAPTATION            FOR THROUGHPUT MAXIMIZATION IN TIME REVERSAL DIVISION            MULTIPLE ACCESS”, filed on Apr. 11, 2016,    -   (d) PCT patent application PCT/US2017/021963, entitled “METHODS,        APPARATUS, SERVERS, AND SYSTEMS FOR VITAL SIGNS DETECTION AND        MONITORING”, filed on Mar. 10, 2017, published as        WO2017/156492A1 on Sep. 14, 2017,        -   (1) which claims priority to U.S. Provisional patent            application 62/307,081, entitled “TR-BREATH: TIME-REVERSAL            BREATHING RATE ESTIMATION AND DETECTION”, filed on Mar. 11,            2016,        -   (2) which claims priority to U.S. Provisional patent            application 62/316,850, entitled “TR-BREATH: TIME-REVERSAL            BREATHING RATE ESTIMATION AND DETECTION”, filed on Apr. 1,            2016,    -   (e) PCT patent application PCT/US2017/021957, entitled “METHODS,        APPARATUS, SERVERS, AND SYSTEMS FOR HUMAN IDENTIFICATION BASED        ON HUMAN RADIO BIOMETRIC INFORMATION”, filed on Mar. 10, 2017,        published as WO2017/156487A1 on Sep. 14, 2017,        -   (1) which claims priority to U.S. Provisional patent            application 62/307,172, entitled “RADIO SHOT:            THROUGH-THE-WALL HUMAN IDENTIFICATION”, filed on Mar. 11,            2016,        -   (2) which claims priority to U.S. Provisional patent            application 62/334,110, entitled “TIME-REVERSAL TRACKING            WITHOUT MAPPING”, filed on May 10, 2016,    -   (f) PCT patent application PCT/US2017/027131, entitled “METHODS,        APPARATUS, SERVERS, AND SYSTEMS FOR OBJECT TRACKING”, filed on        Apr. 12, 2017, published as WO2017/180698A1 on Oct. 19, 2017,        -   (1) which claims priority to U.S. Provisional patent            application 62/322,575, entitled “TIME-REVERSAL RESONATING            EFFECT AND ITS APPLICATION IN WALKING SPEED ESTIMATION”,            filed on Apr. 14, 2016,        -   (2) which claims priority to U.S. Provisional patent            application 62/334,110, entitled “TIME-REVERSAL TRACKING            WITHOUT MAPPING”, filed on May 10, 2016, and        -   (3) which claims priority to U.S. Provisional patent            application 62/409,796, entitled “METHODS, DEVICES, SERVERS,            AND SYSTEMS OF TIME REVERSAL BASED TRACKING”, filed on Oct.            18, 2016,    -   (g) U.S. Provisional patent application 62/557,117, entitled        “METHODS, DEVICES, SERVERS, APPARATUS, AND SYSTEMS FOR WIRELESS        INTERNET OF THINGS APPLICATIONS”, filed on Sep. 11, 2017,    -   (h) U.S. Provisional patent application 62/593,826, entitled        “METHOD, APPARATUS, AND SYSTEM FOR OBJECT TRACKING AND        NAVIGATION”, filed on Dec. 1, 2017,    -   (i) U.S. patent application Ser. No. 15/384,217, entitled        “METHOD, APPARATUS, SERVER, AND SYSTEMS OF TIME-REVERSAL        TECHNOLOGY”, filed on Dec. 19, 2016, published as        US2017/0188359A1 on Jun. 29, 2017,        -   (1) which is a Continuation-in-Part of U.S. patent            application Ser. No. 13/706,342, entitled “WAVEFORM DESIGN            FOR TIME-REVERSAL SYSTEMS,” filed on Dec. 5, 2012, issued as            U.S. Pat. No. 9,883,511 on Jan. 30, 2018,        -   (2) which is a Continuation-in-Part of U.S. patent            application Ser. No. 13/969,271, entitled “TIME-REVERSAL            WIRELESS SYSTEMS HAVING ASYMMETRIC ARCHITECTURE”, filed on            Aug. 16, 2013, published as US2015/0049745A1 on Feb. 19,            2015, issued as U.S. Pat. No. 9,882,675 on Jan. 30, 2018,        -   (3) which is a Continuation-in-Part of U.S. patent            application Ser. No. 13/969,320, entitled “MULTIUSER            TIME-REVERSAL DIVISION MULTIPLE ACCESS UPLINK SYSTEM WITH            PARALLEL INTERFERENCE CANCELLATION”, filed on Aug. 16, 2013,            issued as U.S. Pat. No. 9,559,874 on Jan. 31, 2017,        -   (4) which is a Continuation-in-Part of U.S. patent            application Ser. No. 15/041,677, entitled “HANDSHAKING            PROTOCOL FOR TIME-REVERSAL SYSTEM”, filed on Feb. 11, 2016,            published as US2016/0164669A1 on Jun. 9, 2016, issued as            U.S. Pat. No. 9,794,156 on Oct. 17, 2017,        -   (5) which is a Continuation-in-Part of U.S. patent            application Ser. No. 15/200,430, entitled “QUADRATURE            AMPLITUDE MODULATION FOR TIME-REVERSAL SYSTEMS”, filed on            Jul. 1, 2016, published as US2016/0315797A1 on Oct. 27,            2016, issued as U.S. Pat. No. 9,736,002 on Aug. 15, 2017,            -   a. which is a Continuation of U.S. patent application                Ser. No. 14/262,153, entitled “QUADRATURE AMPLITUDE                MODULATION FOR TIME-REVERSAL SYSTEMS”, filed on Apr. 25,                2014, issued as U.S. Pat. No. 9,407,306 on Aug. 2, 2016,        -   (6) which is a Continuation-in-Part of U.S. patent            application Ser. No. 15/200,429, entitled “TIME-REVERSAL            WIRELESS PARADIGM FOR INTERNET OF THINGS”, filed on Jul. 1,            2016, issued as U.S. Pat. No. 9,781,700 on Oct. 3, 2017,            -   a. which is a Continuation of U.S. patent application                Ser. No. 14/943,648, entitled “TIME-REVERSAL WIRELESS                PARADIGM FOR INTERNET OF THINGS”, filed on Nov. 17,                2015, issued as U.S. Pat. No. 9,402,245 on Jul. 26,                2016,                -   1. which is a Continuation of U.S. patent                    application Ser. No. 14/202,651, entitled                    “TIME-REVERSAL WIRELESS PARADIGM FOR INTERNET OF                    THINGS”, filed on Mar. 10, 2014, issued as U.S. Pat.                    No. 9,226,304 on Dec. 29, 2015,        -   (7) which is a Continuation-in-Part of U.S. patent            application Ser. No. 14/605,611, entitled “WIRELESS            POSITIONING SYSTEM”, filed on Jan. 26, 2015, published as            US2016/0018508A1 on Jan. 21, 2016,            -   a. which claims priority to U.S. Provisional patent                application 62/069,090, entitled “TIME-REVERSAL                POSITIONING SYSTEMS”, filed on Oct. 27, 2014,            -   b. which claims priority to U.S. Provisional patent                application 62/025,795, entitled “TIME-REVERSAL                POSITIONING SYSTEMS”, filed on Jul. 17, 2014,        -   (8) which is a Continuation-in-Part of U.S. patent            application Ser. No. 14/615,984, entitled “JOINT WAVEFORM            DESIGN AND INTERFERENCE PRE-CANCELLATION FOR TIME-REVERSAL            SYSTEMS”, filed on Feb. 6, 2015, issued as U.S. Pat. No.            9,686,054 on Jun. 20, 2017,            -   a. which claims priority to U.S. Provisional patent                application 62/025,795, entitled “TIME-REVERSAL                POSITIONING SYSTEMS”, filed on Jul. 17, 2014,        -   (9) which is a Continuation-in-Part of U.S. patent            application Ser. No. 15/004,314, entitled “TIME-REVERSAL            TECHNOLOGIES FOR HYBRID WIRELESS NETWORKS”, filed on Jan.            22, 2016, issued as U.S. Pat. No. 10,014,982 on Jul. 3,            2018,            -   a. which claims priority to U.S. Provisional patent                application 62/106,395, entitled “TIME-REVERSAL                TECHNOLOGIES FOR HYBRID WIRELESS NETWORKS”, filed on                Jan. 22, 2015,        -   (10) which is a Continuation-in-Part of U.S. patent            application Ser. No. 15/061,059, entitled “TIME-REVERSAL            SCALABILITY FOR HIGH NETWORK DENSIFICATION”, filed on Mar.            4, 2016,            -   a. which claims priority to U.S. Provisional patent                application 62/128,574, entitled “TIME-REVERSAL                SCALABILITY FOR HIGH NETWORK DENSIFICATION”, filed on                Mar. 5, 2015,        -   (11) which is a Continuation-in-Part of PCT patent            application PCT/US2015/041037, entitled “WIRELESS            POSITIONING SYSTEMS”, filed on Jul. 17, 2015, published as            WO2016/011433A2 on Jan. 21, 2016,            -   a. which claims priority to U.S. Provisional patent                application 62/148,019, entitled “WIRELESS POSITIONING                SYSTEMS”, filed on Apr. 15, 2015,            -   b. which is a continuation-in-part of U.S. patent                application Ser. No. 14/605,611, entitled “WIRELESS                POSITIONING SYSTEMS”, filed on Jan. 26, 2015, published                as US2016/0018508A1 on Jan. 21, 2016,                -   1. which claims priority to U.S. Provisional patent                    application 62/025,795 entitled “TIME-REVERSAL                    POSITIONING SYSTEMS”, filed on Jul. 17, 2014, and                -   2. which claims priority to U.S. Provisional patent                    application 62/069,090 entitled “TIME-REVERSAL                    POSITIONING SYSTEMS”, filed on Oct. 27, 2014,        -   (12) which is a Continuation-in-Part of U.S. patent            application Ser. No. 15/268,477, entitled “METHODS, DEVICES            AND SYSTEMS OF HETEROGENEOUS TIME-REVERSAL PARADIGM ENABLING            DIRECT CONNECTIVITY IN INTERNET OF THINGS”, filed on Sep.            16, 2016, issued as U.S. Pat. No. 9,887,864 on Feb. 6, 2018,            -   a. which claims priority to U.S. Provisional patent                application 62/219,315, entitled “ENABLING DIRECT                CONNECTIVITY IN INTERNET OF THINGS: A HETEROGENEOUS                TIME-REVERSAL PARADIGM”, filed on Sep. 16, 2015,            -   b. which is a Continuation-in-part of U.S. patent                application Ser. No. 15/200,429, entitled “TIME-REVERSAL                WIRELESS PARADIGM FOR INTERNET OF THINGS”, filed on Jul.                1, 2016, issued as U.S. Pat. No. 9,781,700 on Oct. 3,                2017,                -   1. which is a Continuation of U.S. patent                    application Ser. No. 14/943,648, entitled                    “TIME-REVERSAL WIRELESS PARADIGM FOR INTERNET OF                    THINGS”, filed on Nov. 17, 2015, issued as U.S. Pat.                    No. 9,402,245 on Jul. 26, 2016,                -    i. which is a Continuation of U.S. patent                    application Ser. No. 14/202,651, entitled                    “TIME-REVERSAL WIRELESS PARADIGM FOR INTERNET OF                    THINGS”, filed on Mar. 10, 2014, issued as U.S. Pat.                    No. 9,226,304 on Dec. 29, 2015,        -   (13) which is a Continuation-in-Part of U.S. patent            application Ser. No. 15/284,496, entitled “TIME-REVERSAL            COMMUNICATION SYSTEMS”, filed on Oct. 3, 2016,            -   a. which claims priority to U.S. Provisional patent                application 62/235,958, entitled “SYMBOL TIMING FOR                TIME-REVERSAL SYSTEMS WITH SIGNATURE DESIGN”, filed on                Oct. 1, 2015,        -   (14) which is a Continuation-in-Part of            -   PCT patent application PCT/US2016/066015, entitled                “METHOD, APPARATUS, AND SYSTEMS FOR WIRELESS EVENT                DETECTION AND MONITORING”, filed on Dec. 9, 2016,                published as WO2017/100706A1 on Jun. 15, 2017, whose US                national stage entry is Ser. No. 16/060,710, filed on                Jun. 8, 2018,            -   a. which claims priority to U.S. Provisional patent                application 62/265,155, entitled “INDOOR EVENTS                DETECTION SYSTEM”, filed on Dec. 9, 2015,            -   b. which claims priority to U.S. Provisional patent                application 62/411,504, entitled “METHOD, APPARATUS, AND                SYSTEM FOR WIRELESS INTERNET OF THINGS APPLICATIONS”,                filed on Oct. 21, 2016,            -   c. which claims priority to U.S. Provisional patent                application 62/383,235, entitled “TIME REVERSAL                MONITORING SYSTEM”, filed on Sep. 2, 2016,            -   d. which claims priority to U.S. Provisional patent                application 62/307,081, entitled “TR-BREATH:                TIME-REVERSAL BREATHING RATE ESTIMATION AND DETECTION”,                filed on Mar. 11, 2016,            -   e. which claims priority to U.S. Provisional patent                application 62/316,850, entitled “TR-BREATH:                TIME-REVERSAL BREATHING RATE ESTIMATION AND DETECTION”,                filed on Apr. 1, 2016,        -   (15) which claims priority to U.S. Provisional patent            application 62/331,278, entitled “USING VIRTUAL ANTENNAS FOR            POWER WAVEFORMING IN WIRELESS POWER TRANSMISSION SYSTEMS”,            filed on May 3, 2016,        -   (16) which claims priority to U.S. Provisional patent            application 62/295,970, entitled “THE IMPACT OF SPATIAL            FOCUSING EFFECTS ON THE MEDIUM ACCESS CONTROL DESIGN FOR            5G”, filed on Feb. 16, 2016,        -   (17) which claims priority to U.S. Provisional patent            application 62/320,965, entitled “OPTIMAL RATE ADAPTATION            FOR THROUGHPUT MAXIMIZATION IN TIME REVERSAL DIVISION            MULTIPLE ACCESS”, filed on Apr. 11, 2016,        -   (18) which claims priority to U.S. Provisional patent            application 62/307,081, entitled “TR-BREATH: TIME-REVERSAL            BREATHING RATE ESTIMATION AND DETECTION”, filed on Mar. 11,            2016,        -   (19) which claims priority to U.S. Provisional patent            application 62/316,850, entitled “TR-BREATH: TIME-REVERSAL            BREATHING RATE ESTIMATION AND DETECTION”, filed on Apr. 1,            2016,        -   (20) which claims priority to U.S. Provisional patent            application 62/307,172, entitled “RADIO SHOT:            THROUGH-THE-WALL HUMAN IDENTIFICATION”, filed on Mar. 11,            2016,        -   (21) which claims priority to U.S. Provisional patent            application 62/322,575, entitled “TIME-REVERSAL RESONATING            EFFECT AND ITS APPLICATION IN WALKING SPEED ESTIMATION”,            filed on Apr. 14, 2016,        -   (22) which claims priority to U.S. Provisional patent            application 62/334,110, entitled “TIME-REVERSAL TRACKING            WITHOUT MAPPING”, filed on May 10, 2016,        -   (23) which claims priority to U.S. Provisional patent            application 62/409,796, entitled “METHODS, DEVICES, SERVERS,            AND SYSTEMS OF TIME REVERSAL BASED TRACKING”, filed on Oct.            18, 2016,        -   (24) which claims priority to U.S. Provisional patent            application 62/383,235, entitled “TIME REVERSAL MONITORING            SYSTEM”, filed on Sep. 2, 2016,        -   (25) which claims priority to U.S. Provisional patent            application 62/384,060, entitled “METHODS, DEVICES, SERVERS,            SYSTEMS OF TIME REVERSAL MACHINE PLATFORM FOR BROADBAND            WIRELESS APPLICATIONS”, filed on Sep. 6, 2016,        -   (26) which claims priority to U.S. Provisional patent            application 62/411,504, entitled “METHOD, APPARATUS, AND            SYSTEM FOR WIRELESS INTERNET OF THINGS APPLICATIONS”, filed            on Oct. 21, 2016,    -   (j) PCT patent application PCT/US2017/015909, entitled “METHODS,        DEVICES, SERVERS, APPARATUS, AND SYSTEMS FOR WIRELESS INTERNET        OF THINGS APPLICATIONS”, filed on Jan. 31, 2017, published as        WO2017/155634A1 on Sep. 14, 2017,        -   (1) which claims priority to U.S. Provisional patent            application 62/384,060, entitled “METHODS, DEVICES, SERVERS,            SYSTEMS OF TIME REVERSAL MACHINE PLATFORM FOR BROADBAND            WIRELESS APPLICATIONS”, filed on Sep. 6, 2016,        -   (2) which claims priority to U.S. Provisional patent            application 62/331,278, entitled “USING VIRTUAL ANTENNAS FOR            POWER WAVEFORMING IN WIRELESS POWER TRANSMISSION SYSTEMS”,            filed on May 3, 2016,        -   (3) which claims priority to U.S. Provisional patent            application 62/307,081, entitled “TR-BREATH: TIME-REVERSAL            BREATHING RATE ESTIMATION AND DETECTION”, filed on Mar. 11,            2016,        -   (4) which claims priority to U.S. Provisional patent            application 62/316,850, entitled “TR-BREATH: TIME-REVERSAL            BREATHING RATE ESTIMATION AND DETECTION”, filed on Apr. 1,            2016,        -   (5) which claims priority to U.S. Provisional patent            application 62/322,575, entitled “TIME-REVERSAL RESONATING            EFFECT AND ITS APPLICATION IN WALKING SPEED ESTIMATION”,            filed on Apr. 14, 2016,        -   (6) which claims priority to U.S. Provisional patent            application 62/334,110, entitled “TIME-REVERSAL TRACKING            WITHOUT MAPPING”, filed on May 10, 2016,        -   (7) which claims priority to U.S. Provisional patent            application 62/409,796, entitled “METHODS, DEVICES, SERVERS,            AND SYSTEMS OF TIME REVERSAL BASED TRACKING”, filed on Oct.            18, 2016,        -   (8) which claims priority to U.S. Provisional patent            application 62/383,235, entitled “TIME REVERSAL MONITORING            SYSTEM”, filed on Sep. 2, 2016,        -   (9) which claims priority to U.S. Provisional patent            application 62/411,504, entitled “METHOD, APPARATUS, AND            SYSTEM FOR WIRELESS INTERNET OF THINGS APPLICATIONS”, filed            on Oct. 21, 2016,        -   (10) which claims priority to U.S. Provisional patent            application 62/307,172, entitled “RADIO SHOT:            THROUGH-THE-WALL HUMAN IDENTIFICATION”, filed on Mar. 11,            2016,        -   (11) which claims priority to PCT patent application            PCT/US2016/066015, entitled “METHOD, APPARATUS, AND SYSTEMS            FOR WIRELESS EVENT DETECTION AND MONITORING”, filed on Dec.            9, 2016, published as WO2017/100706A1 on Jun. 15, 2017,            whose US national stage entry is U.S. patent application            Ser. No. 16/060,710, entitled “METHOD, APPARATUS, AND            SYSTEMS FOR WIRELESS EVENT DETECTION AND MONITORING”, filed            on Jun. 8, 2018,    -   (k) U.S. Provisional patent application 62/678,207, entitled        “METHOD, APPARATUS, AND SYSTEM FOR OBJECT TRACKING AND MOTION        MONITORING”, filed on May 30, 2018,    -   (l) U.S. patent application Ser. No. 15/861,422, entitled        “METHOD, APPARATUS, SERVER, AND SYSTEMS OF TIME-REVERSAL        TECHNOLOGY”, filed on Jan. 3, 2018,    -   (m) U.S. patent application Ser. No. 15/873,806, entitled        “METHOD, APPARATUS, AND SYSTEM FOR OBJECT TRACKING AND        NAVIGATION”, filed on Jan. 17, 2018.    -   (n) U.S. patent application Ser. No. 16/101,444, entitled        “METHOD, APPARATUS, AND SYSTEM FOR WIRELESS MOTION MONITORING”,        filed on Aug. 11, 2018.    -   (o) U.S. Provisional Patent application 62/734,224, entitled        “METHOD, APPARATUS, AND SYSTEM FOR WIRELESS SLEEP MONITORING”,        filed on Sep. 20, 2018.    -   (p) U.S. Provisional Patent application 62/744,093, entitled        “METHOD, APPARATUS, AND SYSTEM FOR WIRELESS PROXIMITY AND        PRESENCE MONITORING”, filed on Oct. 10, 2018.    -   (q) U.S. Provisional Patent application 62/753,017, entitled        “METHOD, APPARATUS, AND SYSTEM FOR HUMAN IDENTIFICATION BASED ON        HUMAN RADIO BIOMETRIC INFORMATION”, filed on Oct. 30, 2018.    -   (r) U.S. patent application Ser. No. 16/200,608, entitled        “METHOD, APPARATUS, SERVER AND SYSTEM FOR VITAL SIGN DETECTION        AND MONITORING”, filed on Nov. 26, 2018.    -   (s) U.S. patent application Ser. No. 16/200,616, entitled        “METHOD, APPARATUS, SERVER AND SYSTEM FOR REAL-TIME VITAL SIGN        DETECTION AND MONITORING”, filed on Nov. 26, 2018.

TECHNICAL FIELD

The present teaching generally relates to event recognition andmonitoring. More specifically, the present teaching relates torecognizing and classifying events in a venue based on a wirelesssignal.

BACKGROUND

Security systems are becoming increasingly prevalent for both publicbuildings and private dwellings. A conventional security systemtypically uses contact sensors that are monitored and/or controlled by acentral panel that is usually mounted in the house. Various sensors canbe installed at windows, doors, and other locations to detect intrusion,e.g. one sensor per door or window. A typical house (e.g. with a size of1500 sqft) may need at least 6-8 sensors. This induces a considerablehardware cost. The installation of the conventional security system(especially the wiring) is tedious, time consuming and expensive. Aconventional security system is very difficult, if not impossible, toupgrade, which is needed over time as technology develops. Maintenanceand repair of a conventional security system are also tedious,expensive, and close to impossible.

In addition, existing security systems based on object motion detectioncannot provide enough accuracy and often lead to false alarms. Existingapproaches include those based on passive infrared (PIR), activeinfrared (AIR) and Ultrasonic. PIR sensors are the most widely usedmotion sensor in home security systems, and can detect human motions bysensing the difference between background heat and the heat emitted bymoving people. However, systems based on PIR sensors are prone to falsealarms due to its sensitivity to environmental changes, like hot/coldair flow and sunlight. They are easily defeated by blocking the bodyheat emission by e.g. wearing a heat-insulating full-body suit. Theirrange is limited and need a line-of-sight (LOS) condition; thus multipledevices are needed. In AIR based systems, an IR emitter sends a beam ofIR which will be received by an IR receiver. When the beam isinterrupted, a motion is detected. This solution can be easily seenusing a regular camera or any IR detection mechanism and also has alimited range and thus needs LOS. Ultrasonic sensors detect human motionby sending out ultrasonic sound waves into a space and measuring thespeed at which they return, and motion can be detected if there is afrequency change. But this approach can be defeated by wearing ananechoic suit. Also, ultrasound cannot penetrate solid objects such asfurniture or boxes, which causes gaps in detection field. Slow movementsby a burglar may not trigger an alarm in this case.

Therefore, there is a need for apparatus and methods for recognizingevents (e.g. events related to object motion and/or security) to solvethe above-mentioned problems and to avoid the above-mentioned drawbacks.

SUMMARY

The present teaching generally relates to periodic motion (e.g. humanbreathing) detection. More specifically, the present teaching relates toperiodic motion detection and monitoring based on time-reversaltechnology in a rich-scattering wireless environment, such as an indoorenvironment or urban metropolitan area, enclosed environment,underground environment, open-air venue with barriers such as parkinglot, storage, yard, square, forest, cavern, valley, etc.

In one embodiment, a method implemented on a machine having a processor,a memory communicatively coupled with the processor and a set ofinstructions stored in the memory for recognizing an event is disclosed.The method comprises: for each of at least one known event happening ina venue in a respective training time period: transmitting, by anantenna of a first transmitter, a respective training wireless signal toat least one first receiver through a wireless multipath channelimpacted by the known event in the venue in the training time periodassociated with the known event, obtaining, asynchronously by each ofthe at least one first receiver based on the training wireless signal,at least one time series of training channel information (training CItime series) of the wireless multipath channel between the firstreceiver and the first transmitter in the training time periodassociated with the known event, and pre-processing the at least onetraining CI time series; training at least one classifier for the atleast one known event based on the at least one training CI time series;and for a current event happening in the venue in a current time period,transmitting, by an antenna of a second transmitter, a current wirelesssignal to at least one second receiver through the wireless multipathchannel impacted by the current event in the venue in the current timeperiod associated with the current event, obtaining, asynchronously byeach of the at least one second receiver based on the current wirelesssignal, at least one time series of current channel information (currentCI time series) of the wireless multipath channel between the secondreceiver and the second transmitter in the current time periodassociated with the current event, pre-processing the at least onecurrent CI time series, and applying the at least one classifier to:classify at least one of: the at least one current CI time series, aportion of a particular current CI time series, and a combination of theportion of the particular current CI time series and a portion of anadditional CI time series, and associate the current event with at leastone of: a known event, an unknown event and another event. A training CItime series associated with a first receiver and a current CI timeseries associated with a second receiver have at least one of: differentstarting times, different time durations, different stopping times,different counts of items in their respective time series, differentsampling frequencies, different sampling periods between two consecutiveitems in their respective time series, and channel information (CI) withdifferent features.

In another embodiment, a system having a processor, a memorycommunicatively coupled with the processor and a set of instructionsstored in the memory for recognizing an event is disclosed. The systemcomprises a first transmitter and a second transmitter in a venue, atleast one first receiver and at least one second receiver in the venue,and an event recognition engine. The first transmitter is configuredfor: for each of at least one known event happening in the venue in arespective training time period, transmitting a respective trainingwireless signal through a wireless multipath channel impacted by theknown event in the venue in the training time period associated with theknown event. Each of the at least one first receiver is configured for:for each of the at least one known event happening in the venue,receiving asynchronously the respective training wireless signal throughthe wireless multipath channel, obtaining, asynchronously based on therespective training wireless signal, at least one time series oftraining channel information (training CI time series) of the wirelessmultipath channel between the first receiver and the first transmitterin the training time period associated with the known event, andpre-processing the at least one training CI time series. The secondtransmitter is configured for: for a current event happening in thevenue in a current time period, transmitting a current wireless signalthrough the wireless multipath channel impacted by the current event inthe venue in the current time period associated with the current event.Each of the at least one second receiver is configured for: for thecurrent event happening in the venue in the current time period,receiving asynchronously the current wireless signal through thewireless multipath channel, obtaining, asynchronously based on thecurrent wireless signal, at least one time series of current channelinformation (current CI time series) of the wireless multipath channelbetween the second receiver and the second transmitter in the currenttime period associated with the current event, and pre-processing the atleast one current CI time series. The event recognition engine isconfigured for: training at least one classifier for the at least oneknown event based on the at least one training CI time series; andapplying the at least one classifier to: classify at least one of: theat least one current CI time series, a portion of a particular currentCI time series, and a combination of the portion of the particularcurrent CI time series and a portion of an additional CI time series,and associate the current event with at least one of: a known event, anunknown event and another event. A training CI time series associatedwith a first receiver and a current CI time series associated with asecond receiver have at least one of: different starting times,different time durations, different stopping times, different counts ofitems in their respective time series, different sampling frequencies,different sampling periods between two consecutive items in theirrespective time series, and channel information (CI) with differentfeatures.

In yet another embodiment, an event recognition engine of a wirelessmonitoring system is disclosed. The system comprises a first transmitterand a second transmitter in a venue, at least one first receiver and atleast one second receiver in the venue, and the event recognitionengine. The event recognition engine comprises: a processor, a memorycommunicatively coupled with the processor, and a set of instructionsstored in the memory. The set of instructions, when executed, causes theprocessor to perform: for each of at least one known event happening ina venue, obtaining, from each of at least one first receiver in thevenue, at least one time series of training channel information(training CI time series) of a wireless multipath channel, wherein thefirst receiver extracts the training CI time series from a respectivetraining wireless signal received from a first transmitter in the venuethrough the wireless multipath channel between the first receiver andthe first transmitter in a training time period associated with theknown event, training at least one classifier for the at least one knownevent based on the at least one training CI time series, for a currentevent happening in the venue in a current time period, obtaining, fromeach of at least one second receiver in the venue, at least one timeseries of current channel information (current CI time series) of thewireless multipath channel impacted by the current event, wherein thesecond receiver extracts the current CI time series from a currentwireless signal received from a second transmitter in the venue throughthe wireless multipath channel between the second receiver and thesecond transmitter in the current time period associated with thecurrent event, and applying the at least one classifier to: classify atleast one of: the at least one current CI time series, a portion of aparticular current CI time series, and a combination of the portion ofthe particular current CI time series and a portion of an additional CItime series, and associate the current event with at least one of: aknown event, an unknown event and another event. A training CI timeseries associated with a first receiver and a current CI time seriesassociated with a second receiver have at least one of: differentstarting times, different time durations, different stopping times,different counts of items in their respective time series, differentsampling frequencies, different sampling periods between two consecutiveitems in their respective time series, and channel information (CI) withdifferent features.

In still another embodiment, a receiver of a wireless monitoring systemis disclosed. The receiver comprises: a wireless circuitry, a processorcommunicatively coupled with the wireless circuitry, a memorycommunicatively coupled with the processor, and a set of instructionsstored in the memory. The wireless circuitry is configured to: for eachof at least one known event happening in a venue, receive a respectivetraining wireless signal through a wireless multipath channel, whereinthe respective training wireless signal is transmitted by a firsttransmitter through the wireless multipath channel between the receiverand the first transmitter in a training time period associated with theknown event, and for a current event happening in the venue in a currenttime period, receive a current wireless signal through the wirelessmultipath channel impacted by the current event, wherein the currentwireless signal is transmitted by a second transmitter through thewireless multipath channel between the receiver and the secondtransmitter in the current time period associated with the currentevent. The set of instructions, when executed, causes the processor to:for each of the at least one known event happening in the venue, obtain,asynchronously based on the respective training wireless signal, atleast one time series of training channel information (training CI timeseries) of the wireless multipath channel, and for the current eventhappening in the venue in the current time period, obtain,asynchronously based on the current wireless signal, at least one timeseries of current channel information (current CI time series) of thewireless multipath channel. The at least one training CI time series isused by an event recognition engine of the wireless monitoring system totrain at least one classifier for the at least one known event. The atleast one classifier is applied to: classify at least one of: the atleast one current CI time series, a portion of a particular current CItime series, and a combination of the portion of the particular currentCI time series and a portion of an additional CI time series, andassociate the current event with at least one of: a known event, anunknown event and another event.

Other concepts are related to software for implementing the presentteaching on recognizing security related events based on wirelesschannel information in a rich-scattering environment.

Additional novel features will be set forth in part in the descriptionwhich follows, and in part will become apparent to those skilled in theart upon examination of the following and the accompanying drawings ormay be learned by production or operation of the examples. The novelfeatures of the present teachings may be realized and attained bypractice or use of various aspects of the methodologies,instrumentalities and combinations set forth in the detailed examplesdiscussed below.

BRIEF DESCRIPTION OF DRAWINGS

The methods, systems, and/or programming described herein are furtherdescribed in terms of exemplary embodiments. These exemplary embodimentsare described in detail with reference to the drawings. Theseembodiments are non-limiting exemplary embodiments, in which likereference numerals represent similar structures throughout the severalviews of the drawings.

FIG. 1 illustrates an exemplary network environment for event detectionand monitoring in a venue, according to one embodiment of the presentteaching.

FIG. 2 illustrates an exemplary diagram of a device in a wirelessmonitoring system, according to one embodiment of the present teaching.

FIG. 3 illustrates an exemplary flow of detecting indoor events usingtime-reversal technology, according to one embodiment of the presentteaching.

FIG. 4 illustrates an exemplary scheme for breathing signal extractionand maximization, according to one embodiment of the present teaching.

FIG. 5 illustrates an exemplary breathing signal based on real-worldmeasurements, according to one embodiment of the present teaching.

FIG. 6 demonstrates gains of a disclosed scheme for breathing signalextraction and maximization, according to one embodiment of the presentteaching.

FIG. 7A and FIG. 7B illustrate comparison results between a wake stateand a sleep state, according to one embodiment of the present teaching.

FIG. 8A and FIG. 8B illustrate breathing rate performances of differentsleep stages, according to one embodiment of the present teaching.

FIG. 9 illustrates an exemplary network environment for sleepmonitoring, according to one embodiment of the present teaching.

FIG. 10 illustrates an exemplary algorithm design for sleep monitoring,according to one embodiment of the present teaching.

FIG. 11 illustrates an exemplary demonstration of a Smart TV use case,according to one embodiment of the present teaching.

FIG. 12 illustrates an exemplary setting of Smart TV with deployment ofdevices under the TV, according to one embodiment of the presentteaching.

FIG. 13 illustrates an exemplary experiment setting of Smart TV witharea partition in front of the TV, according to one embodiment of thepresent teaching.

FIG. 14 illustrates an exemplary setting of Smart TV with deployment ofdevices inside the TV, according to one embodiment of the presentteaching.

FIG. 15 illustrates an exemplary setting of Smart TV with Origin and Botinstalled in a speaker close to the TV, according to one embodiment ofthe present teaching.

FIG. 16 illustrates an exemplary setting of Smart TV with Origin and Botinstalled on a table close to the TV, according to one embodiment of thepresent teaching.

FIG. 17 illustrates an exemplary setting of Smart fan with Origin andBot installed on the high stand of the fan, according to one embodimentof the present teaching.

FIG. 18 illustrates an exemplary setting of Smart car with Origin andBot installed outside of the car, according to one embodiment of thepresent teaching.

FIG. 19 illustrates an exemplary interior bird-view of a car for seatoccupancy detection and people counting, according to one embodiment ofthe present teaching.

FIGS. 20A-20D illustrate changes of channel information according tovarious seat occupancy situations in a car, according to one embodimentof the present teaching.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant teachings. However, it should be apparent to those skilledin the art that the present teachings may be practiced without suchdetails. In other instances, well known methods, procedures, components,and/or circuitry have been described at a relatively high-level, withoutdetail, in order to avoid unnecessarily obscuring aspects of the presentteachings.

The present teaching discloses systems, apparatus, and methods forrecognizing events (e.g. events related to object motion and/orsecurity) in a venue based on a time series of channel information (CI)of a wireless multipath channel that is impacted by the object motion.According to various embodiments, the disclosed system can be integratedinto a security system to monitor opening of front door (e.g. byintruders or by dementia patients), opening of window, etc. Thedisclosed system can be trained to know “when the door is opened”, “whenthe door is closed”, etc.

In one embodiment, the system uses discrete training to recognize: “doorfully open”, “door fully close”, and “door half open”. In anotherembodiment, the system is trained for continuous monitoring, and may usedynamic time warping (DTW) to monitor the door movement continuously sothat every position of the door (e.g. x % open with x being between 0and 100) can be detected and recognized.

In one embodiment, the disclosed system has hardware components (e.g.wireless transmitter/receiver with antenna, analog circuitry, powersupply, processor, memory, etc.) and corresponding software components.According to various embodiments of the present teaching, the disclosedsystem includes a Bot (referred to as a Type 1 device) and an Origin(referred to as a Type 2 device) for transient motion detection andmonitoring. Each device comprises a transceiver, a processor and amemory.

The disclosed system is easy and simple to install with a low hardwarecost. In one embodiment, the disclosed system only needs two wirelessdevices (a Type 1 device and a Type 2 device) to cover a 1500-sqfthouse. The wireless devices are plug-and-play, and can be plugged intowall power sockets in opposite corner of the house. The disclosed systemcan be improved over time with machine learning, and can be upgraded viaremote software upgrade. The system can do software diagnosis, whichmakes its maintenance and repair an easy job.

The disclosed system includes features that are significantly more thanan abstract idea. A security system with low cost and high accuracy hasbeen desired for a long time. The disclosed system solves the long-timeproblem using physical WiFi chips as sensors to monitor the environmentwith WiFi signals and the associated multipath patterns. The disclosedsystem has hardware components (e.g. Type 1 device, Type 2 device eachwith processor, memory, wireless transceivers, cloud server, etc.). TheWiFi signals are electromagnetic (EM) waves in the air which aremeasurable and with deterministic structure and frequencycharacteristics. The system has matching software components (e.g.embedded software in Type 1 device, in Type 2 device, in servers, etc.).The software can interface with low-level WiFi chips and firmware toextract CSI. Experimental results show that the disclosed system canrecognize security related events and motions with very high accuracy.

The disclosed system can be applied in many cases. In one example, theType 1 device (transmitter) may be a small WiFi-enabled device restingon the table. It may also be a WiFi-enabled television (TV), set-top box(STB), a smart speaker (e.g. Amazon echo), a smart refrigerator, a smartmicrowave oven, a mesh network router, a mesh network satellite, a smartphone, a computer, a tablet, a smart plug, etc. In one example, the Type2 (receiver) may be a WiFi-enabled device resting on the table. It mayalso be a WiFi-enabled television (TV), set-top box (STB), a smartspeaker (e.g. Amazon echo), a smart refrigerator, a smart microwaveoven, a mesh network router, a mesh network satellite, a smart phone, acomputer, a tablet, a smart plug, etc. The Type 1 device and Type 2devices may be in a home security system for an owner of a house todetect any intrusion and any unexpected activity in and around thehouse. The Type 1 device and Type 2 devices may be placed in bedrooms,bathrooms, baby-rooms, restrooms to monitor unexpected intrusion orsuspicious activities. The Type 1 device and Type 2 devices may bedeployed in an area to detect sudden or unexpected motions indicatingany intruder or accident.

Hardware modules may be constructed to contain the Type 1 transceiverand/or the Type 2 transceiver. The hardware modules may be sold to/usedby variable brands to design, build and sell final commercial products.Products using the disclosed system and/or method may be home/officesecurity products, motion monitoring products, WiFi products, meshproducts, TV, STB, entertainment system, HiFi, speaker, home appliance,lamps, stoves, oven, microwave oven, table, chair, bed, shelves, tools,utensils, torches, vacuum cleaner, smoke detector, sofa, piano, fan,door, window, door/window handle, locks, smoke detectors, caraccessories, computing devices, office devices, air conditioner, heater,pipes, connectors, surveillance camera, access point, computing devices,mobile devices, LTE devices, 3G/4G/5G/6G devices, gaming devices,eyeglasses, glass panels, VR goggles, necklace, watch, waist band, belt,wallet, pen, hat, wearables, implantable device, tags, parking tickets,smart phones, etc.

In addition, sleep monitoring acts as a critical and challenging taskthat attracts increasing demands and interests. The present teachingdiscloses the model, design, and implementation of SMARS (SleepMonitoring via Ambient Radio Signals), which is the first practicalSleep Monitoring system that exploits Ambient Radio Signals to recognizesleep stages and assess sleep quality. This will enable a future smarthome that monitors daily sleep in a ubiquitous, non-invasive andcontactless manner, without instrumenting the subject's body or the bed.Different from previous RF-based approaches, the present teachingdevises a statistical model that accounts for all reflecting andscattering multipaths, allowing highly accurate and instantaneousbreathing estimation with ever best performance achieved on commoditydevices. On this basis, SMARS then recognizes different sleep stages,including wake, REM, and NREM, which is previously only possible withdedicated hardware. A real-time system is implemented on commercial WiFichipsets and deployed in 6 homes with 6 participants, resulting in 32nights of data in total. The results demonstrate that SMARS yields amedian error of 0.47 bpm and a 95% error of only 2.92 bpm for breathingestimation, and detects breathing robustly even when a person is 10 maway from the link, or behind a wall. SMARS achieves sleep stagingaccuracy of 85%, outperforming advanced solutions using contact sensoror radar. Furthermore, SMARS is evaluated upon a recently releaseddataset that measures 20 patients' overnight sleep, which confirms theperformance. By achieving promising results with merely a singlecommodity RF link, SMARS will set the stage for a practical in-homesleep monitoring solution.

Sleep plays a vital role in an individual's health and well-being, bothmentally and physically. It is well recognized that sleep quantity andquality is fundamentally related to health risks like cardiovasculardecease, stroke, kidney failure, diabetes, and adverse mentalconditions, etc. Unfortunately, in modern society, a number of peoplesuffer from sleep disorders. As recently reported, 10% of the populationsuffers from chronic insomnia (which is even higher among elders), and ⅓of Americans do not get sufficient sleep. Monitoring sleep emerges as anessential demand to help, manage, diagnose, and treat the growing groupof sleep disorders as well as to keep regular tabs on personal health.

Sleep monitoring, however, is a challenging task that has drawntremendous efforts for decades. Generally, it measures sleep time,recognizes different sleep stages, e.g., wake, REM (Rapid Eye Movement)and NREM (Non-REM), and accordingly assesses an individual's sleepquality. Various solutions have been proposed. The medical gold standardrelies on Polysomnography (PSG), which monitors various physiologicalparameters such as brain activities, respirations, and body movements bya number of wired sensors attached to the patient. Albeit accurate andcomprehensive, PSG is usually expensive and cumbersome with the invasivesensors that may cause sleep difficulties, limiting itself to clinicalusage for confirmed patients. Other approaches includingphotoplethysmography (PPG) and actigraphy (ACT) require users to weardedicated sensors during sleep. Ballistocardiogram (BCG) needs toinstrument the mattress with an array of EMFi sensors to measureballistic force. Despite of the costs, these approaches provide suitablesolutions for those who need special cares but are less-than-ideal forthe public. Recent efforts in mobile computing envision in-home sleepmonitoring using smartphones and wearables. These methods, however, onlyprovide coarse-grained, less accurate measurements and fail to monitorvital signs like respiratory rate. In addition, mobiles and wearablesare undesirable for especially elders and those with dementia.

Different from prevailing solutions, the disclosed solution looksforward to a future smart home that monitors daily sleep in aubiquitous, non-invasive, contactless, and accurate manner, withoutinstrumenting the body or the bed. One can observe an opportunitytowards such a system by two perspectives: 1) Clinical study has shownthat physiological activity varies among different sleep stages. Forexample, breathing rate becomes irregular and fast since brain oxygenconsumption increases during REM sleep, and is more stable and slowerduring NREM sleep, rendering the feasibility of sleep staging based onbreathing monitoring. 2) Recent advances in wireless technology havedemonstrated non-contact sensing of body motions in the environments.Chest and abdomen motions caused by breathing can alter radio signalpropagations and thus modulate the received signals, from which it isthen possible to decipher breathing. One can explore a synergy betweenthe two perspectives, resulting in a system to leverage ambient radiosignals (e.g., WiFi) to capture a person's breathing and motion duringsleep and further monitor the sleep behaviors.

While early works have investigated the feasibility of RF-basedbreathing estimation and sleep monitoring, they either rely onspecialized hardware like FMCW radar, or only work in controlledenvironments. Solutions based on dedicated radios are usually expensiveand not ubiquitously applicable. Others using commodity devicestypically require the user to lay still on a bed with radios exceedinglyclose to his/her chest and fail in presence of extraneous motions or inNon-Line-Of-Sight (NLOS) scenarios. In addition, none of them canidentify different sleep stages due to their limited accuracy inbreathing estimation. Such limitations prevent them from application forpractical in-home sleep monitoring.

The present teaching disclose the model, design, and implementation ofSMARS, the first practical Sleep Monitoring system that exploitscommodity Ambient Radio Signals to recognize sleep stages and assess theotherwise elusive sleep quality. SMARS works in a non-obtrusive waywithout any body contact. All that a user needs to do is to set up onesingle link between two commodity radios by, e.g., simply placing areceiver if a wireless router is already installed inside the home.SMARS advances the literature by a novel statistical model that allowshighly accurate and instantaneous breathing estimation. On this basis,SMARS is able to distinguish different sleep stages, which is previouslyonly obtainable by expensive dedicated hardware. Specifically, SMARSexcels in three unique aspects to deliver practical sleep monitoring.First, one can devise a statistical model on motion in Channel StateInformation (CSI) that leverages all reflection and scatteringmultipaths indoors. Existing works usually assume a geometrical modelwith a few multipaths and one major path reflected off the human body(e.g., using a 2-ray model developed for outdoor space). Underreal-world indoor environments, however, there could be as many ashundreds of multipaths, and signals not only reflect but also scatteroff a person's body and other objects in the space. As a consequence,previous approaches fail in NLOS environments and for minute motions dueto the lack of a dominant reflection path. In contrast, the disclosedmodel investigates the statistical characteristics of motion in CSIwithout making such unrealistic assumptions, underlying robust detectionof arbitrary motions including breathing.

Second, SMARS achieves accurate respiratory rate estimationinstantaneously and robustly. Most of previous breathing estimationschemes assume constant breathing rate during a relatively large timewindow to gain sufficient frequency resolution, losing fine-grainedbreathing variations during sleep. In addition, minute breathing motionscan be easily buried in CSI measurement noises, rendering existingphilosophies effective only in extraordinary close proximity (typicallywithin 2-3 m) without any extraneous motions. To improve timeresolution, SMARS exploits the time-domain Auto-Correlation Function(ACF) to estimate breathing rate, which can report real-time breathingrates as frequent as every one second and make it possible to captureinstantaneous breathing rate changes. By using ACF, SMARS alsocircumvents the use of noisy phase and the usually handcrafted CSIdenoising procedure. More importantly, by eliminating the frequencyoffsets and thus synchronizing breathing signal over differentsubcarriers, ACF allows us to perform Maximal Ratio Combining (MRC) tocombine multiple subcarriers to combat measurement noises and maximizebreathing signals in an optimal way. By doing so, one can push the limitof the breathing signal to noise ratio (SNR) and thus significantlyincrease the sensing sensitivity for larger coverage as well as weakerbreathing. Specifically, SMARS can reliably detect breathing when aperson is 10 m away from the link, or behind a wall, which is evenbetter than specialized low-power radars.

Finally, based on the extracted breathing rates and motion statisticsduring sleep, one can recognize different sleep stages (including wake,REM and NREM) and comprehensively assess the overall sleep quantity andquality. Based on in-depth understanding of the relationship betweenbreathing rates and sleep stages, one can extract distinctive breathingfeatures for classification for sleep staging. None of existing worksusing off-the-shelf devices can achieve the same goal of staging sleep.

A real-time system has been implemented on different commercial WiFichipsets and its performance is evaluated through extensive experiments.The evaluation includes two parts: 1) One can deploy SMARS in 6 homeswith 6 healthy subjects and collect 32 nights of data, 5 out of whichhave PSG data recorded by commercial devices. The results show thatSMARS achieves great performance, with a median breathing estimationerror of 0.47 breath per minute (bpm) and a 95% tile error of 2.92 bmp.Regarding sleep staging, SMARS produces a remarkable accuracy of 85.2%,while commercial solutions, e.g., EMFIT based on contact sensors andResMed using radar, have accuracies of only 69.8% and 83.7%respectively. 2) One can further validate SMARS on a recently releaseddataset on RF-based respiration monitoring. The dataset collected 20patients' overnight sleep for comparative evaluation of fourstate-of-the-art breathing monitoring systems, using clinically labeledPSG data as ground truths. All the four systems (based on ZigBee,Sub-RSS radio, UWB radar, and WiFi CSI, respectively) producesignificant median errors of about 2-3 bpm and 95% tile errors of aroundor above 10 bpm. As comparison, SMARS achieves significant improvementsby decreasing the median error to 0.66 bpm and the 95% tile error to3.79 bpm. By achieving promising performance, SMARS can deliverclinically meaningful sleep monitoring for daily and regular use inpractice and takes an important step towards a future smart home thatmonitors personal health everyday life.

In a nutshell, the core contribution here is SMARS, the first systemthat enables a smart home to stage an inhabitant's sleep using commodityoff-the-shelf WiFi devices, by achieving highly accurate andinstantaneous breathing estimation in the wild. SMARS also contributesthe first statistical model for understanding and capturing motions inCSI, which will renovate various applications in wireless sensing.

In one embodiment, the present teaching discloses a method, anapparatus, a device, a system, and/or software(method/apparatus/device/system/software) of a wireless monitoringsystem. A time series of channel information (CI) of a wirelessmultipath channel may be obtained using a processor, a memorycommunicatively coupled with the processor and a set of instructionsstored in the memory. The time series of CI may be extracted from awireless signal transmitted between a Type 1 heterogeneous wirelessdevice and a Type 2 heterogeneous wireless device in a venue through thewireless multipath channel. The wireless multipath channel may beimpacted by a motion of an object in the venue. A characteristics and/ora spatial-temporal information of the object and/or of the motion of anobject may be monitored based on the time series of CI. A task may beperformed based on the characteristics and/or the spatial-temporalinformation. A presentation associated with the task may be generated ina user-interface (UI) on a device of a user. The time series of CI maybe preprocessed.

The Type 1 device may comprise at least one heterogeneous wirelesstransmitter. The Type 2 device may comprise at least one heterogeneouswireless receiver. The Type 1 device and the Type 2 device may be thesame device. Any device may have a processor, a memory communicativelycoupled with the processor, and a set of instructions stored in thememory to be executed by the processor.

There may be multiple Type 1 heterogeneous wireless devices interactingwith the same Type 2 heterogeneous wireless device, and/or there may bemultiple Type 2 heterogeneous wireless devices interacting with the sameType 1 heterogeneous wireless device. The multiple Type 1 devices/Type 2devices may be synchronized and/or asynchronous, with same/differentwindow width/size and/or time shift. The multiple Type 1 devices/Type 2devices may operate independently and/or collaboratively. The multipleType 1 devices/Type 2 devices may be communicatively coupled to same ordifferent servers (e.g. cloud server, edge server, local server).Operation of one device may be based on operation, state, internalstate, storage, processor, memory output, physical location, computingresources, network of another device. Difference devices may communicatedirectly, and/or via another device/server/cloud server. The devices maybe associated with one or more users, with associated settings. Thesettings may be chosen once, pre-programmed, and/or changed over time.

In the case of multiple Type 1 devices interacting with multiple Type 2devices, any processing (e.g. time domain processing and/or frequencydomain processing) may be different for different devices. Theprocessing may be based on locations, orientation, direction, roles,user-related characteristics, settings, configurations, availableresources, available bandwidth, network connection, hardware, software,processor, co-processor, memory, battery life, available power,antennas, antenna types, directional/unidirectional characteristics ofthe antenna, power setting, and/or other parameters/characteristics ofthe devices.

The wireless receiver (e.g. Type 2 device) may receive the wirelesssignal and/or another wireless signal from the wireless transmitter(e.g. Type 1 device). The wireless receiver may receive another wirelesssignal from another wireless transmitter (e.g. a second Type 1 device).The wireless transmitter may transmit the wireless signal and/or anotherwireless signal to another wireless receiver (e.g. a second Type 2device). The wireless transmitter, the wireless receiver, the anotherwireless receiver and/or the another wireless transmitter may be movingwith the object and/or another object. The another object may betracked.

The Type 1 device may be capable of wirelessly coupling with at leasttwo Type 2 devices. The Type 2 device may be capable of wirelesslycoupling with at least two Type 1 devices. The Type 1 device may becaused to switch/establish wireless coupling from the Type 2 device to asecond Type 2 heterogeneous wireless device at another location in thevenue. Similarly, the Type 2 device may be caused to switch/establishwireless coupling from the Type 1 device to a second Type 1heterogeneous wireless device at yet another location in the venue. Theswitching may be controlled by a server, the processor, the Type 1device, the Type 2 device, and/or another device. The radio used beforeswitching may be different from the radio used after the switching. Asecond wireless signal may be caused to be transmitted between the Type1 device and the second Type 2 device (or between the Type 2 device andthe second Type 1 device) through the wireless multipath channel. Asecond time series of CI of the wireless multipath channel extractedfrom the second wireless signal may be obtained. The second wirelesssignal may be the first wireless signal. The characteristics, thespatial-temporal information and/or another quantity of the motion ofthe object may be monitored based on the second time series of CI. TheType 1 device and the Type 2 device may be the same device.

The wireless signal and/or the another wireless signal may have dataembedded. The wireless receiver, the wireless transmitter, the anotherwireless receiver and/or the another wireless transmitter may beassociated with at least one processor, memory communicatively coupledwith respective processor, and/or respective set of instructions storedin the memory which when executed cause the processor to perform anyand/or all steps needed to determine the spatial-temporal information,the initial spatial-temporal information, the initial time, thedirection, the instantaneous location, the instantaneous angle, and/orthe speed, of the object.

The processor, the memory and/or the set of instructions may beassociated with the Type 1 heterogeneous wireless transceiver, one ofthe at least one Type 2 heterogeneous wireless transceiver, the object,a device associated with the object, another device associated with thevenue, a cloud server, and/or another server.

The Type 1 device may transmit the wireless signal (e.g. a series ofprobe signals) in a broadcasting manner to at least one Type 2 device(s)through the wireless multipath channel in the venue. The wireless signalis transmitted without the Type 1 device establishing wirelessconnection with any of the at least one Type 2 device receiver(s).

The Type 1 device may transmit to a particular destination media accesscontrol (MAC) address common for more than one Type 2 devices. Each ofthe more than one Type 2 devices may adjust their MAC address to theparticular MAC address.

The particular destination MAC address (common destination MAC address)may be associated with the venue. The association may be recorded in anassociation table of an Association Server. The venue may be identifiedby the Type 1 device, a Type 2 device and/or another device based on theparticular destination MAC address, the series of probe signals, and/orthe at least one time series of CI extracted from the probe signals.

For example, a Type 2 device may be moved to a new location in the venue(e.g. from another venue). And the Type 1 device may be newly set up inthe venue such that the Type 1 device and the Type 2 device are notaware of the existence of each other, and they may not establishwireless connection at all. During set up, the Type 1 device may beinstructed/guided/caused/controlled to send the series of probe signalsto the particular destination MAC address (e.g. using a dummy receiver,using a hardware pin setting/connection, using a stored setting, using alocal setting, using a remote setting, using a downloaded setting, orusing a server). Upon power up, the Type 2 device may scan for probesignals according to a table of destination MAC addresses that may beused at different locations (e.g. different MAC address for differentvenue such as house, office, enclosure, floor, multi-storey building,store, airport, mall, stadium, hall, station, subway, lot, area, zone,region, district, city, country, continent). When the Type 2 devicedetects the probe signals sent to the particular destination MACaddress, the Type 2 device can use the table to identify the venue basedon the particular destination MAC address.

A location of a Type 2 device in the venue may be computed based on theparticular destination MAC address, the series of probe signals, and/orthe at least one time series of CI obtained by the Type 2 device fromthe probe signals. The computing may be performed by the Type 2 device.

The particular destination MAC address may be changed over time. It maybe changed according to a time table, a rule, a policy, a mode, acondition, a situation and/or a change. The particular destination MACaddress may be selected based on availability of the MAC address, apre-selected list, collision pattern, traffic pattern, data trafficbetween the Type 1 device and another device, effective bandwidth,random selection, and/or a MAC address switching plan. The particulardestination MAC address may be the MAC address of a second wirelessdevice (e.g. a dummy receiver, or a receiver that serves as a dummyreceiver).

The Type 1 device may transmit the probe signals in a channel selectedfrom a set of channels. At least one CI of the selected channel may beobtained by a respective Type 2 device from the probe signal transmittedin the selected channel.

The selected channel may be changed over time. The change may beaccording to a time table, a rule, a policy, a mode, a condition, asituation, and/or a change. The selected channel may be selected basedon availability of channels, a pre-selected list, co-channelinterference, inter-channel interference, channel traffic patterns, datatraffic between the Type 1 device and another device, effectivebandwidth associated with channels, a security criterion, randomselection, a channel switching plan, a criterion, and/or aconsideration.

The particular destination MAC address and/or an information of theselected channel may be communicated between the Type 1 device and aserver through a network. The particular destination MAC address and/orthe information of the selected channel may also be communicated betweena Type 2 device and a server through another network. The Type 2 devicemay communicate the particular destination MAC address and/or theinformation of the selected channel to another Type 2 device (e.g. via amesh network, via Bluetooth, via WiFi, etc). The particular destinationMAC address and/or the information of the selected channel may be chosenby a server. The particular destination MAC address and/or theinformation of the selected channel may be signaled in an announcementchannel by the Type 1 device and/or a server. Before the particulardestination MAC address and/or the information of the selected channelis being communicated, it may be pre-processed.

Wireless connection between the Type 1 device and another wirelessdevice may be established. The Type 1 device may send a first wirelesssignal (e.g. a sounding frame, a probe signal, a request-to-send RTS) tothe another wireless device. The another wireless device may reply bysending a second wireless signal (e.g. a command, or a clear-to-sendCTS) to the Type 1 device, triggering the Type 1 device to transmit thewireless signal (e.g. series of probe signals) in the broadcastingmanner to the at least one Type 2 device without establishing wirelessconnection with any of the at least one Type 2 device. The secondwireless signal may be a response or an acknowledge (e.g. ACK) to thefirst wireless signal. The second wireless signal may contain a datawith information of at least one of: the venue, and the Type 1 device.The first wireless signal may also be in response to the second wirelesssignal.

The another wireless device may be a dummy wireless device with apurpose (e.g. a primary purpose, a secondary purpose) to establish thewireless connection with the Type 1 device, to receive the firstwireless signal, and/or to send the second wireless signal. The anotherwireless device may be physically attached to the Type 1 device.

In another example, the another wireless device may send a thirdwireless signal to the Type 1 device triggering the Type 1 device totransmit the wireless signal (e.g. series of probe signals) in thebroadcasting manner to the at least one Type 2 device withoutestablishing wireless connection with any of the at least one Type 2device. The Type 1 device may reply to the third wireless signal bytransmitting a fourth wireless signal to the another wireless device.

The another wireless device may not communicate further with the Type 1device after establishing the connection with the Type 1 device. Theanother wireless device may suspend communication with the Type 1 deviceafter establishing the connection. The communication may be resumed inthe future. The another wireless device may enter an inactive mode, ahibernation mode, a sleep mode, a stand-by mode, a low-power mode, anOFF mode and/or a power-down mode, after establishing the wirelessconnection with the Type 1 device.

The another wireless device may have the particular destination MACaddress. It may use the wireless connection to trigger the Type 1 deviceto send the at least one series of probe signals in a broadcastingmanner to the particular destination MAC address. Each of the at leastone Type 2 device may set its MAC address to the particular destinationMAC address so as to receive the at least one series of probe signalsfrom the Type 1 device. A new Type 2 device in the venue may obtain theparticular destination MAC address from a designated source (e.g. acloud server, an edge server, a remote server, a local server, a webserver, an internet server, a webpage, a database) and set its MACaddress to the particular destination MAC address so as to receive theat least one series of probe signals from the Type 1 device

Both the Type 1 device and the another wireless device may be controlledand/or coordinated by at least one of: a first processor associated withthe Type 1 device, a second processor associated with the anotherwireless device, a third processor associated with the designated sourceand/or a fourth processor associated with another device. The firstprocessor and the second processor may coordinate with each other.

A first series of probe signals may be transmitted by a first antenna ofthe Type 1 device to at least one first Type 2 device through a firstwireless multipath channel in a first venue. A second series of probesignals may be transmitted by a second antenna of the Type 1 device toat least one second Type 2 device through a second wireless multipathchannel in a second venue. The first series and the second seriesmay/may not be different. The at least one first Type 2 device may/maynot be different from the at least one second Type 2 device. The firstseries and/or the second series of probe signals may be transmitted in abroadcasting manner. The first antenna may/may not be different from thesecond antenna.

The Type 1 device may send the two separate series of probe signals totwo separate groups of Type 2 devices located in two venues. The firstset of Type 2 devices (the at least one first Type 2 device) may receivethe first time series of probe signals through a first wirelessmultipath channel of the first venue. The second set of Type 2 devices(the at least one second Type 2 device) may receive the second timeseries of probe signals through a second wireless multipath channel inthe second venue. If in broadcasting mode, Type 1 device may notestablish wireless connection with any of the at least one first andsecond Type 2 devices.

The two venues may have different sizes, shape, multipathcharacteristics. The first venue and the second venue may overlappartially. The immediate area around the first antenna and secondantenna may be part of the overlapped area. The first venue may be asubset of the second venue, or vice versa. The first wireless multipathchannel and the second wireless multipath channel may be different. Forexample, the first one may be WiFi while the second may be LTE. Or, bothmay be WiFi, but the first one may be 2.4 GHz WiFi and the second may be5 GHz WiFi. Or, both may be 2.4 GHz WiFi, but may have different channelnumbers, with different SSID names, and WiFi settings (e.g.encryption—the first may use PKA and the second may use PKA2).

Each first Type 2 device may obtain at least one first time series of CIfrom the first series of probe signals, the CI being of the firstwireless multipath channel between the first Type 2 device and the Type1 device. Each second Type 2 device may obtain at least one second timeseries of CI from the second series of probe signals, the CI being ofthe second wireless multipath channel between the second Type 2 deviceand the Type 1 device.

The first antenna may/may not be the second antenna. The at least onefirst Type 2 device may/may not be the same as the at least one secondType 2 device. A particular first Type 2 device may be the same as aparticular second Type 2 device. The first and second series of probesignals may/may not be synchronous. The transmission of the first seriesof probe signals may not be synchronous to the transmission of thesecond series of probe signals. A probe signal may be transmitted withdata. A probe signal may be replaced by a data signal.

The first series of probe signals may be transmitted at a firstpseudo-regular interval (e.g. at a rate of 30 Hz). The second series ofprobe signals may be transmitted at a second pseudo-regular interval(e.g. at a rate of 300 Hz). The first pseudo-regular interval may/maynot be different from the second pseudo-regular interval. The firstand/or second pseudo-regular interval may be changed over time. Thechange may be according to at least one of: a time table, a rule, apolicy, a mode, a condition, a situation, and/or a change. Anypseudo-regular interval may be changed over time.

The first series of probe signals may be transmitted to a firstdestination MAC address. The second series of probe signals may betransmitted to a second destination MAC address. The two destination MACaddresses may/may not be different. Any of the two destination MACaddresses may be changed over time. The change may be according to atleast one of: a time table, a rule, a policy, a mode, a condition, asituation, and/or a change.

The first series of probe signals may be transmitted in a first channel.The second series of probe signals may be transmitted in a secondchannel. The first channel and the second channel may/may not bedifferent. The first channel and/or the second channel may be changedover time. The change may be according to at least one of: a time table,a rule, a policy, a mode, a condition, a situation, and/or a change.

A first wireless connection may be established between the Type 1 deviceand another wireless device. The Type 1 device may establish the firstwireless connection with the another wireless device. The Type 1 devicemay send a first wireless signal to the another wireless device. Theanother wireless device may send a second wireless signal to the Type 1device. One of the first wireless signal and the second wireless signalmay be in response to the other. The second wireless signal to the Type1 device may trigger the Type 1 device to transmit the first series ofprobe signals in the broadcasting manner. The second wireless signal maybe an acknowledge to the first wireless signal.

A second wireless connection may be established between the Type 1device and yet another wireless device. The Type 1 device may establishthe second wireless connection with the yet another wireless device. TheType 1 device may send a third wireless signal to the yet anotherwireless device. The yet another wireless device may send a fourthwireless signal to the Type 1 device. One of the third wireless signaland the fourth wireless signal may be in response to the other. Thefourth wireless signal to the Type 1 device may trigger the Type 1device to transmit the second series of probe signals in thebroadcasting manner. The fourth wireless signal may be an acknowledge tothe third wireless signal. The third wireless signal may be similar tothe first wireless signal. The fourth wireless signal may be similar tothe second wireless signal. The yet another wireless device may be theanother wireless device. The third wireless signal may be sent by theType 1 device before the first wireless communication is fullyestablished and/or completed.

The another wireless device and the yet another wireless device may bethe same device, which may perform signal handshake (e.g. the handshakewith the first wireless signal and second wireless signal, or thehandshake with the third signal and the fourth signal) with all Type 1devices sequentially to trigger them to transmit in the broadcastingmanner. The signal handshake with the Type 1 devices may be performed inat least one of: a sequential manner, a parallel manner and a mixedmanner (partially sequential, partially parallel).

The Type 1 device, the another wireless device and/or the yet anotherwireless device may be controlled and/or coordinated, physicallyattached, or may be of/in/of a common device. At least two of the Type 1device, the another wireless device and/or the yet another wirelessdevice may be controlled by/connected to a common data processor, or maybe connected to a common bus interconnect/network/LAN/Bluetoothnetwork/BLE network/wired network/wireless network/mesh network/mobilenetwork/cloud, or may share a common memory, or may be associated with acommon user/user profile/user account/identity(ID)/household/house/physical address/location/geographic coordinate/IPsubnet/SSID/user device/home device/office device/manufacturing device.

The Type 1 device, the another wireless device and/or the yet anotherwireless device may each be associated a processor, a memorycommunicatively coupled with the processor, and a set of instructionstored in the memory to be executed by the processor. The Type 1 device,the another wireless device and/or the yet another wireless device mayeach comprise a processor, a memory communicatively coupled with theprocessor, and a set of instruction stored in the memory to be executedby the processor. When executed, the set of instructions may cause thedevice to perform an operation. Their processors may be coordinated.

At least one series of probe signals may be transmitted asynchronouslyand contemporaneously by each of more than one Type 1 heterogeneouswireless devices. Each respective series of probe signals is transmittedby an antenna of respective Type 1 heterogeneous wireless device in abroadcasting manner to a respective set of Type 2 devices through awireless multipath channel, without the respective Type 1 deviceestablishing wireless connection with any of the respective set of Type2 receivers.

The transmitting may be using a respective processor, a respectivememory and a respective set of instructions. Each of the more than oneType 1 heterogeneous wireless devices may have a heterogeneousintegrated circuit (IC). Each of the respective set of Type 2 device mayhave a respective heterogeneous IC.

Each Type 1 device may be the signal source of a set of respective Type2 devices (i.e. the Type 1 device sends a respective wireless signal(series of probe signals) to the set of respective Type 2 devices. Eachrespective Type 2 device chooses the Type 1 device from among all Type 1devices as its signal source. Each Type 2 device may chooseasynchronously (at different time). At least one time series of CI maybe obtained by each respective Type 2 device from the respective seriesof probe signals from the Type 1 device, the CI being of the wirelessmultipath channel between the Type 2 device and the Type 1 device.

The respective Type 2 device chooses the Type 1 device from among allType 1 devices as its signal source based on at least one of: identity(ID) of the Type 1 device and/or the respective Type 2 receiver,information of at least one of: a user, an account, access info, aparameter, a characteristics, and a signal strength associated with theType 1 device and/or the respective Type 2 receiver, a task to beperformed by the Type 1 device and/or the respective Type 2 receiver,whether the Type 1 device is the same as a past signal source, a history(of the past signal source, the Type 1 device, another Type 1 device,the respective Type 2 receiver, and another Type 2 receiver), and/or atleast one threshold for switching signal source.

Initially, the Type 1 device may be the signal source of a set ofinitial respective Type 2 devices (i.e. the Type 1 device sends arespective wireless signal (series of probe signals) to the set ofinitial respective Type 2 devices) at an initial time. Each initialrespective Type 2 device chooses the Type 1 device from among all Type 1devices as its signal source.

The signal source (Type 1 device) of a particular Type 2 device may bechanged when at least one of the following occurs: (1) a time intervalbetween two adjacent probe signals (e.g. between the current probesignal and an immediate past probe signal, or between the next probesignal and the current probe signal) received from the current signalsource of the particular Type 2 device exceeds a first threshold, (2) asignal strength associated with the current signal source of the Type 2device is below a second threshold at the respective current time, (3) aprocessed signal strength associated with the current signal source ofthe Type 2 device is below a second threshold at the respective currenttime after the signal strength is processed with at least one of: a lowpass filter, a band pass filter, a median filter, a moving averagefilter, a weighted averaging filter, a linear filter and a non-linearfilter, and/or (4) the signal strength (or processed signal strength)associated with the current signal source of the Type 2 device is belowa fourth threshold for a significant percentage of a recent time window(e.g. 70%, 80%, 90%, etc.). The percentage may exceed a fifth threshold.Any of first threshold, second threshold, third threshold, fourththreshold and fifth threshold may be time varying.

Condition (1) may occur when the Type 1 device and the Type 2 devicebecome progressively far away from each other, such that some probesignal from the Type 1 device becomes too weak and is not received bythe Type 2 device. Conditions (2)-(4) may occur when the two devicesbecome far from each other such that the signal strength becomes veryweak.

The signal source of the particular Type 2 device may not change if theother Type 1 devices have signal strength weaker than the current signalsource (current selected Type 1 device). In another example, the signalsource of the particular Type 2 device may also not change if the otherType 1 devices have signal strength weaker than a factor (e.g. 1.1, 1.2,or 1.5, etc.) of signal strength of the current signal source.

If the signal source is changed, the new signal source may take effectat a near future time (e.g. the respective next time). The new signalsource may be the Type 1 device with strongest signal strength. The newsignal source may also be the Type 1 device with strongest processedsignal strength, wherein the signal strength of each Type 1 device maybe processed with at least one of: a low pass filter, a band passfilter, a median filter, a moving average filter, a weighted averagingfilter, a linear filter and a non-linear filter.

A list of available Type 1 devices may be initialized. The list may beupdated by examining signal strength associated with the respective setof Type 1 devices. The list of available Type 1 devices may also beupdated by examining processed signal strength associated with therespective set of Type 1 devices.

The respective chosen Type 1 device may/may not be different from thecurrent signal source.

A Type 2 device may choose between the first series of probe signals andthe second series of probe signals based on: respective pseudo-regularintervals, respective destination MAC addresses, respective channels,respective characteristics/properties/states, a respective task to beperformed by the Type 2 device, signal strength of first series andsecond series, and/or another consideration.

The yet another wireless device and the another wireless device may bethe same device. They may be in/on/of a common device. The Type 1 devicemay first establish the first wireless communication with the anotherwireless device, and then establish the second wireless communicationwith the yet another wireless device. The Type 1 device may startestablishing the second wireless communication with the yet anotherwireless device before the first wireless communication is fullyestablished. The Type 1 device may start establishing the secondwireless communication with the yet another wireless device while thefirst wireless communication is being established.

The another wireless device may set its MAC address to a firstdestination MAC address and establish the first wireless connection withthe Type 1 device using the first destination MAC address. The yetanother wireless device may set its MAC address to a second destinationMAC address and establish the second wireless connection with the Type 1device using the second destination MAC address.

The transmitting may be using a processor, a memory and a set ofinstructions. Each of the Type 1 device and/or at least one Type 2device may also have a heterogeneous IC. The heterogeneous IC maycomprise the processor, memory and the set of instructions. Theheterogeneous IC in different devices may be the same or different. EachType 2 device may extract at least one time series of CI of the wirelessmultipath channel between the Type 1 device and the Type 2 device fromthe wireless signal. Each time series of CI is associated with anantenna of the Type 1 device and an antenna of the Type 2 device. Theheterogeneous IC may generate the wireless signal, transmit or receivethe wireless signal, extract the time series of CI from the wirelesssignal, and make the time series of CI available (e.g. to theprocessor/memory, other processors/memory).

Multiple Type 1 devices may transmit wireless signals asynchronously(e.g. to the same Type 2 device, to a number of Type 2 devices).

Multiple Type 2 devices may receive the wireless signal asynchronously.They may extract respective time series of CI from the wireless signalasynchronously, with respective heterogeneous starting time.

The wireless signal may be a series of probe signals. A probe signal maybe transmitted with data. A probe signal may be replaced by a datasignal.

The series of probe signals may be transmitted at a pseudo-regularinterval (e.g. 100 probe signals per second). The pseudo-regularinterval may be changed. The series of probe signals may be scheduled ata regular interval (e.g. 100 probe signals per second), but each probesignal may experience small time perturbation, perhaps due to timingrequirement, timing control, network control, handshaking, messagepassing, collision avoidance, carrier sensing, congestion, availabilityof resources, and/or another consideration.

The series of probe signals may be transmitted at the pseudo-regularinterval for a period of time. The pseudo-regular interval, the durationof the period of time and/or the starting time of the period of time,may be changed over time. For example, it may be 100 probe signals persecond (probe/sec in short) for 2 hours and then changed to 1000probe/sec for 30 minutes, and may change again.

The change may be according to a time table, a rule, a policy, a mode, acondition and/or a change (e.g. once every hour, or changed whenever acertain event occur). For example, the pseudo-regular interval maynormally be 100 probe/sec, but will be changed to 1000 probe/sec in somedemanding situations, and may be changed to 10 probe/sec in some lowpower and/or standby situation. The probe signals may be sent in burstalso, e.g. at 100 probe/sec for 1 minute and no signal for some timebefore and after the burst. Or, the probe signal may be sent at 100probe/sec and then momentarily at a burst rate of 1000 probe/sec for 1minute and then back to 100 probe/sec. In another example, the probesignal may be sent at 1 probe/sec in a low power mode, at 10 probe/secin a standby mode, at 100 probe/sec in a normal mode, and at 1000probe/sec in an armed mode, etc.

The change based on at least one task performed by Type 1 device or Type2 device. For example, the task of one receiver may need 10 probe/secwhile the task of another receiver may need 1000 probe/sec. Thepseudo-regular interval may be increased to 1000 probe/sec to satisfythe most demanding receiver. In one example, the receivers, the tasksassociated with the receivers and/or the wireless multipath channel asexperienced by the receivers may be divided into at least one class(e.g. a low priority class, a high priority class, an emergency class, acritical-task class, a regular class, a privileged class, anon-subscription class, a subscription class, a paying class, anon-paying class, etc.) and the pseudo-regular interval may be adjustedfor the sake of some selected class (e.g. the high priority class). Whenthe need of that selected class is less demanding, the pseudo-regularinterval may be changed (increased or decreased). When a receiver hascritically low power, the pseudo-regular interval may be reduced toreduce the power consumption of the receiver to respond to the probesignals.

In one example, the probe signals may be used to transfer powerwirelessly to a receiver, and the pseudo-regular interval may beadjusted to control the amount of power being wirelessly transferred tothe receiver.

The pseudo-regular interval may be changed by and/or based on a server,the Type 1 device and/or the Type 2 device. The server may becommunicatively coupled with the Type 1 device and/or the Type 2 device.The change may be communicated between the server, the Type 1 deviceand/or the Type 2 device. For example, the server may monitor, track,forecast and/or anticipate the needs of the Type 2 device and/or thetasks performed by the receivers, and may decide to change thepseudo-regular interval accordingly. The server may send the changerequest to the Type 1 device (immediately or with some time delay) andthe Type 1 device may then make the change. The server may keep track ofa time table and may make scheduled changes to the pseudo-regularinterval according to the time table. The server may detect an emergencysituation of a receiver and change the pseudo-regular intervalimmediately. The server may detect a slowly happening condition (and/oran evolving condition) associated with a receiver and gradually adjustthe pseudo-regular interval accordingly.

There may be more than one Type 1 devices. Each Type 1 device maytransmit a respective wireless signal to more than one respective Type 2devices through the wireless multipath channel in the venue. A CI timeseries may be obtained, each CI time series associated with a Type 1device and a Type 2 device.

The characteristics and/or the spatial-temporal information of themotion of the object may be monitored individually based on a CI timeseries associated with a particular Type 1 device and a particular Type2 device.

The characteristics and/or the spatial-temporal information of themotion of the object may be monitored jointly based on any CI timeseries associated with the particular Type 1 device and any Type 2device.

The characteristics and/or the spatial-temporal information of themotion of the object may also be monitored jointly based on any CI timeseries associated with the particular Type 2 device and any Type 1device.

The characteristics and/or the spatial-temporal information of themotion of the object may be monitored globally based on any CI timeseries associated with any Type 1 device and any Type 2 device.

Any joint monitoring (of the same motion or of the same object) may beassociated with at least one of: a user, a user account, a profile, ahousehold, a room, a location, and/or a user history, etc.

A first wireless multipath channel between a Type 1 device and a Type 2device may be different from a second wireless multipath channel betweenanother Type 1 device and another Type 2 device. The two wirelessmultipath channels may be associated with different frequency bands,different bandwidth, different carrier frequency, different modulation,different wireless standards, different coding, different encryption,different payload characteristics, different networks, different networkparameters, etc.

The first wireless multipath channel and the second wireless multipathchannel may be associated with different kinds of wireless system (e.g.two of the following: WiFi, LTE, LTE-A, 2.5G, 3G, 3.5G, 4G, beyond 4G,5G, 6G, 7G, a 802.11 system, a 802.15 system, a 802.16 system, meshnetwork, Zigbee, WiMax, Bluetooth, BLE, RFID, UWB, microwave system,radar like system, etc.). For example, the first may be WiFi while thesecond may be LTE.

The first wireless multipath channel and the second wireless multipathchannel may be associated with similar kinds of wireless system, but indifferent network. For example, the first wireless multipath channel maybe associated with a WiFi network named “Pizza and Pizza” in the 2.4 GHzband with a bandwidth of 20 MHz while the second may be associated witha WiFi network called “StarBud hotspot” in the 5 GHz band with abandwidth of 40 MHz.

The first wireless multipath channel may be associated with a particularwireless system (e.g. WiFi, or LTE, or 3G, or BLE, or mesh network, oradhoc network) while the second wireless multipath channel may beassociated another wireless system of same kind as the first wirelessmultipath channel (e.g. both WiFi, or both LTE, or both 3G, etc.) withdifferent network ID, SSID, characteristics, settings, and/orparameters. The first wireless multipath channel may also be a WiFichannel while the second wireless multipath channel may be another WiFichannel. For example, both first and second wireless multipath channelsmay be in the same WiFi network named “StarBud hotspot”, using twodifferent WiFi channels in “StarBud hotspot”.

In one embodiment, a wireless monitoring system may comprise training atleast one classifier of at least one known events in a venue based ontraining CI time series associated with the at least one events.

For each of the at least one known event happening in the venue in arespective training time period associated with the known event, arespective training wireless signal (e.g. a respective series oftraining probe signals) may be transmitted by an antenna of a first Type1 heterogeneous wireless device using a processor, a memory and a set ofinstructions of the first Type 1 device to at least one first Type 2heterogeneous wireless device through a wireless multipath channel inthe venue in the respective training time period.

At least one respective time series of training CI (training CI timeseries) may be obtained asynchronously by each of the at least one firstType 2 device from the (respective) training wireless signal. The CI maybe CI of the wireless multipath channel between the first Type 2 deviceand the first Type 1 device in the training time period associated withthe known event. The at least one training CI time series may bepreprocessed.

For a current event happening in the venue in a current time period, acurrent wireless signal (e.g. a series of current probe signals) may betransmitted by an antenna of a second Type 1 heterogeneous wirelessdevice using a processor, a memory and a set of instructions of thesecond Type 1 device to at least one second Type 2 heterogeneouswireless device through the wireless multipath channel impacted by thecurrent event in the venue in the current time period associated withthe current event.

At least one time series of current CI (current CI time series) may beobtained asynchronously by each of the at least one second Type 2 devicefrom the current wireless signal (e.g. the series of current probesignals). The CI may be CI of the wireless multipath channel between thesecond Type 2 device and the second Type 1 device in the current timeperiod associated with the current event. The at least one current CItime series may be preprocessed.

The at least one classifier may be applied to classify at least onecurrent CI time series obtained from the series of current probe signalsby the at least one second Type 2 device, to classify at least oneportion of a particular current CI time series, and/or to classify acombination of the at least one portion of the particular current CItime series and another portion of another CI time series. The at leastone classifier may also be applied to associate the current event with aknown event, an unknown event and/or another event.

Each wireless signal (e.g. each series of probe signals) may comprise atleast two CI each with an associated time stamp. Each CI may beassociated with a respective time stamp.

A current CI time series associated with a second Type 2 device andanother current CI time series associated with another second Type 2device may have different starting time, different time duration,different stopping time, different count of items in the time series,different sampling frequency, different sampling period between twoconsecutive items in the time series, and/or CI (CI) with differentfeatures.

The first Type 1 device and the second Type 1 device may be the samedevice. The first Type 1 device and the second Type 1 device may be atsame location in the venue.

The at least one first Type 2 device and the at least one second Type 2device may be the same. The at least one first Type 2 device may be apermutation of the at least one second Type 2 device. A particular firstType 2 device and a particular second Type 2 device may be the samedevice.

The at least one first Type 2 device and/or a subset of the at least onefirst Type 2 device may be a subset of the at least one second Type 2device. The at least one second Type 2 device and/or a subset of the atleast one second Type 2 device may be a subset of the at least one firstType 2 device.

The at least one first Type 2 device and/or a subset of the at least onefirst Type 2 device may be a permutation of a subset of the at least onesecond Type 2 device. The at least one second Type 2 device and/or asubset of the at least one second Type 2 device may be a permutation ofa subset of the at least one first Type 2 device.

The at least one second Type 2 device and/or a subset of the at leastone second Type 2 device may be at same respective location as a subsetof the at least one first Type 2 device. The at least one first Type 2device and/or a subset of the at least one first Type 2 device may be atsame respective location as a subset of the at least one second Type 2device.

The antenna of the Type 1 device and the antenna of the second Type 1device may be at same location in the venue. Antenna(s) of the at leastone second Type 2 device and/or antenna(s) of a subset of the at leastone second Type 2 device may be at same respective location asrespective antenna(s) of a subset of the at least one first Type 2device. Antenna(s) of the at least one first Type 2 device and/orantenna(s) of a subset of the at least one first Type 2 device may be atsame respective location(s) as respective antenna(s) of a subset of theat least one second Type 2 device.

A first section of a first time duration of the first CI time series anda second section of a second time duration of the second section of thesecond CI time series may be aligned. A map between items of the firstsection and items of the second section may be computed.

The first CI time series may be processed by a first operation.

The second CI time series may be processed by a second operation.

The first operation and/or the second operation may comprise at leastone of: subsampling, re-sampling, interpolation, filtering,transformation, feature extraction, pre-processing, and/or anotheroperation.

A first item of the first section may be mapped to a second item of thesecond section. The first item of the first section may also be mappedto another item of the second section. Another item of the first sectionmay also be mapped to the second item of the second section. The mappingmay be one-to-one, one-to-many, many-to-one, many-to-many.

At least one function of at least one of: the first item of the firstsection of the first CI time series, another item of the first CI timeseries, a time stamp of the first item, a time difference of the firstitem, a time differential of the first item, a neighboring time stamp ofthe first item, another time stamp associated with the first item, thesecond item of the second section of the second CI time series, anotheritem of the second CI time series, a time stamp of the second item, atime difference of the second item, a time differential of the seconditem, a neighboring time stamp of the second item, and another timestamp associated with the second item, may satisfy at least oneconstraint.

One constraint may be that a difference between the time stamp of thefirst item and the time stamp of the second item may be upper-bounded byan adaptive upper threshold and lower-bounded by an adaptive lowerthreshold.

The first section may be the entire first CI time series. The secondsection may be the entire second CI time series. The first time durationmay be equal to the second time duration.

A section of a time duration of a CI time series may be determinedadaptively. A tentative section of the CI time series may be computed. Astarting time and an ending time of a section (e.g. the tentativesection, the section) may be determined. The section may be determinedby removing a beginning portion and an ending portion of the tentativesection.

A beginning portion of a tentative section may be determined as follows.Iteratively, items of the tentative section with increasing time stampmay be considered as a current item, one item at a time.

In each iteration, at least one activity measure may be computed and/orconsidered. The at least one activity measure may be associated with atleast one of: the current item associated with a current time stamp,past items of the tentative section with time stamps not larger than thecurrent time stamp, and/or future items of the tentative section withtime stamps not smaller than the current time stamp. The current itemmay be added to the beginning portion of the tentative section if atleast one criterion associated with the at least one activity measure issatisfied.

The at least one criterion associated with the activity measure maycomprise at least one of: (a) the activity measure is smaller than anadaptive upper threshold, (b) the activity measure is larger than anadaptive lower threshold, (c) the activity measure is smaller than anadaptive upper threshold consecutively for at least a predeterminedamount of consecutive time stamps, (d) the activity measure is largerthan an adaptive lower threshold consecutively for at least anotherpredetermined amount of consecutive time stamps, (e) the activitymeasure is smaller than an adaptive upper threshold consecutively for atleast a predetermined percentage of the predetermined amount ofconsecutive time stamps, (f) the activity measure is larger than anadaptive lower threshold consecutively for at least anotherpredetermined percentage of the another predetermined amount ofconsecutive time stamps, (g) another activity measure associated withanother time stamp associated with the current time stamp is smallerthan another adaptive upper threshold and larger than another adaptivelower threshold, (h) at least one activity measure associated with atleast one respective time stamp associated with the current time stampis smaller than respective upper threshold and larger than respectivelower threshold, (i) percentage of time stamps with associated activitymeasure smaller than respective upper threshold and larger thanrespective lower threshold in a set of time stamps associated with thecurrent time stamp exceeds a threshold, and (j) another criterion.

An activity measure associated with an item at time T1 may comprise atleast one of: (1) a first function of the item at time T1 and an item attime T1−D1, wherein D1 is a pre-determined positive quantity (e.g. aconstant time offset), (2) a second function of the item at time T1 andan item at time T1+D1, (3) a third function of the item at time T1 andan item at time T2, wherein T2 is a pre-determined quantity (e.g. afixed initial reference time; T2 may be changed over time; T2 may beupdated periodically; T2 may be the beginning of a time period and T1may be a sliding time in the time period), and (4) a fourth function ofthe item at time T1 and another item.

At least one of: the first function, the second function, the thirdfunction, and/or the fourth function may be a function (e.g. F(X, Y, . .. )) with at least two arguments: X and Y.

The two arguments may be scalars. The function (e.g. F) may be afunction of at least one of: X, Y, (X−Y), (Y−X), abs(X−Y), X^a, Y^b,abs(X^a−Y^b), (X−Y)^a, (X/Y), (X+a)/(Y+b), (X^a/Y^b), and ((X/Y)^a−b),wherein a and b are may be some predetermined quantities. For example,the function may simply be abs(X−Y), or (X−Y)^2, (X−Y)^4. The functionmay be a robust function. For example, the function may be (X−Y)^2 whenabs (X−Y) is less than a threshold T, and (X−Y)+a when abs(X−Y) islarger than T. Alternatively, the function may be a constant whenabs(X−Y) is larger than T. The function may also be bounded by a slowlyincreasing function when abs(X−y) is larger than T, so that outlierscannot severely affect the result. Another example of the function maybe (abs(X/Y)−a), where a=1. In this way, if X=Y (i.e. no change or noactivity), the function will give a value of 0. If X is larger than Y,(X/Y) will be larger than 1 (assuming X and Y are positive) and thefunction will be positive. And if X is less than Y, (X/Y) will besmaller than 1 and the function will be negative.

In another example, both arguments X and Y may be n-tuples such thatX=(x_1, x_2, . . . , x_n) and Y=(y_1, y_2, . . . , y_n). The functionmay be a function of at least one of: x_i, y_i (x_i−y_i), (y_i−x_i),abs(x_i−y_i), x_i^a, y_i^b, abs(x_i^a−y_i^b), (x_i−y_i)^a, (x_i/y_i),(x_i+a)/(y_i+b), (x_i^a/y_i^b), and ((x_i/y_i)^a−b), wherein i is acomponent index of the n-tuple X and Y, and 1<=i<=n. E.g. componentindex of x_1 is i=1, component index of x_2 is i=2.

The function may comprise a component-by-component summation of anotherfunction of at least one of the following: x_i, y_i, (x_i—y_i),(y_i−x_i), abs(x_i−y_i), x_i^a, y_i^b, abs(x_i^a−y_i^b), (x_i−y_i)^a,(x_i/y_i), (x_i+a)/(y^i+b), (x_i^a/y_i^b), and ((x_i/y_i)^a−b), whereini is the component index of the n-tuple X and Y. For example, thefunction may be in a form of sum {i=1}^n (abs(x_i/y_i)−1)/n, orsum_{i=1}^n w_i*(abs(x_i/y_i)−1), where w_i is some weight for componenti.

The map may be computed using dynamic time warping (DTW). The DTW maycomprise a constraint on at least one of: the map, the items of thefirst CI time series, the items of the second CI time series, the firsttime duration, the second time duration, the first section, and/or thesecond section. Suppose in the map, the i^{th} domain item is mapped tothe j^{th} range item. The constraint may be on admissible combinationof i and j (constraint on relationship between i and j).

Mismatch cost between a first section of a first time duration of afirst CI time series and a second section of a second time duration of asecond CI time series may be computed.

The first section and the second section may be aligned such that a mapcomprising more than one links may be established between first items ofthe first CI time series and second items of the second CI time series.With each link, one of the first items with a first time stamp may beassociated with one of the second items with a second time stamp.

A mismatch cost between the aligned first section and the aligned secondsection may be computed. The mismatch cost may comprise a function of:an item-wise cost between a first item and a second item associated by aparticular link of the map, and a link-wise cost associated with theparticular link of the map.

The aligned first section and the aligned second section may berepresented respectively as a first vector and a second vector of samevector length. The mismatch cost may comprise at least one of: an innerproduct, an inner-product-like quantity, a quantity based oncorrelation, a quantity based on covariance, a discriminating score, adistance, a Euclidean distance, an absolute distance, an Lk distance(e.g. L1, L2, . . . ), a weighted distance, a distance-like quantityand/or another similarity value, between the first vector and the secondvector. The mismatch cost may be normalized by the respective vectorlength.

A parameter derived from the mismatch cost between the first section ofthe first time duration of the first CI time series and the secondsection of the second time duration of the second CI time series may bemodeled with a statistical distribution. At least one of: a scaleparameter, a location parameter and/or another parameter, of thestatistical distribution may be estimated.

The first section of the first time duration of the first CI time seriesmay be a sliding section of the first CI time series. The second sectionof the second time duration of the second CI time series may be asliding section of the second CI time series.

A first sliding window may be applied to the first CI time series and acorresponding second sliding window may be applied to the second CI timeseries. The first sliding window of the first CI time series and thecorresponding second sliding window of the second CI time series may bealigned.

Mismatch cost between the aligned first sliding window of the first CItime series and the corresponding aligned second sliding window of thesecond CI time series may be computed. The current event may beassociated with at least one of: the known event, the unknown eventand/or the another event, based on the mismatch cost.

The classifier may be applied to at least one of: each first section ofthe first time duration of the first CI time series, and/or each secondsection of the second time duration of the second CI time series, toobtain at least one tentative classification results. Each tentativeclassification result may be associated with a respective first sectionand a respective second section.

The current event may be associated with at least one of: the knownevent, the unknown event and/or the another event, based on the mismatchcost.

The current event may be associated with at least one of: the knownevent, the unknown event and/or the another event, based on a largestnumber of tentative classification results in more than one sections ofthe first CI time series and corresponding more than sections of thesecond CI time series. For example, the current event may be associatedwith a particular known event if the mismatch cost points to theparticular known event for N consecutive times (e.g. N=10). In anotherexample, the current event may be associated with a particular knownevent if the percentage of mismatch cost within the immediate past Nconsecutive N pointing to the particular known event exceeds a certainthreshold (e.g. >80%).

In another example, the current event may be associated with a knownevent that achieves smallest mismatch cost for the most times within atime period. The current event may be associated with a known event thatachieves smallest overall mismatch cost, which is a weighted average ofat least one mismatch cost associated with the at least one firstsections. The current event may be associated with a particular knownevent that achieves smallest of another overall cost.

The current event may be associated with the “unknown event” if none ofthe known events achieve mismatch cost lower than a first threshold T1in a sufficient percentage of the at least one first section. Thecurrent event may also be associated with the “unknown event” if none ofthe events achieve an overall mismatch cost lower than a secondthreshold T2.

The current event may be associated with at least one of: the knownevent, the unknown event and/or the another event, based on the mismatchcost and additional mismatch cost associated with at least oneadditional section of the first CI time series and at least oneadditional section of the second CI time series.

The known events may comprise at least one of: a door closed event, adoor open event, a window closed event, a window open event, amulti-state event, an on-state event, an off-state event, anintermediate state event, a continuous state event, a discrete stateevent, a human-present event, a human-absent event, asign-of-life-present event, and/or a sign-of-life-absent event.

A projection for each CI may be trained using a dimension reductionmethod based on the training CI time series. The dimension reductionmethod may comprise at least one of: principal component analysis (PCA),PCA with different kernel, independent component analysis (ICA), Fisherlinear discriminant, vector quantization, supervised learning,unsupervised learning, self-organizing maps, auto-encoder, neuralnetwork, deep neural network, and/or another method. The projection maybe applied to at least one of: the training CI time series associatedwith the at least one event, and/or the current CI time series, for theat least one classifier.

The at least one classifier of the at least one event may be trainedbased on the projection and the training CI time series associated withthe at least one event. The at least one current CI time series may beclassified based on the projection and the current CI time series.

The projection may be re-trained using at least one of: the dimensionreduction method, and another dimension reduction method, based on atleast one of: the projection before the re-training, the training CItime series, at least one current CI time series before retraining theprojection, and/or additional training CI time series.

The another dimension reduction method may comprise at least one of: asimplified dimension reduction method, principal component analysis(PCA), PCA with different kernels, independent component analysis (ICA),Fisher linear discriminant, vector quantization, supervised learning,unsupervised learning, self-organizing maps, auto-encoder, neuralnetwork, deep neural network, and/or yet another method,

The at least one classifier of the at least one event may be re-trainedbased on at least one of: the re-trained projection, the training CItime series associated with the at least one events, and/or at least onecurrent CI time series.

The at least one current CI time series may be classified based on: there-trained projection, the re-trained classifier, and/or the current CItime series.

Each CI may comprise a vector of complex values. Each complex value maybe preprocessed to give the magnitude of the complex value. Each CI maybe preprocessed to give a vector of non-negative real numbers comprisingthe magnitude of corresponding complex values.

Each training CI time series may be weighted in the training of theprojection.

The projection may comprise more than one projected components. Theprojection may comprise at least one most significant projectedcomponent. The projection may comprise at least one projected componentthat may be beneficial for the at least one classifier.

The channel information (CI) may be associated with/may comprise signalstrength, signal amplitude, signal phase, received signal strengthindicator (RSSI), channel state information (CSI), a channel impulseresponse (CIR), a channel frequency response (CFR). The CI may beassociated with information associated with a frequency band, afrequency signature, a frequency phase, a frequency amplitude, afrequency trend, a frequency characteristics, a frequency-likecharacteristics, a time domain element, a frequency domain element, atime-frequency domain element, an orthogonal decompositioncharacteristics, and/or a non-orthogonal decomposition characteristicsof the wireless signal through the wireless multipath channel.

The CI may also be associated with information associated with a timeperiod, a time signature, a time stamp, a time amplitude, a time phase,a time trend, and/or a time characteristics of the wireless signal. TheCI may be associated with information associated with a time-frequencypartition, a time-frequency signature, a time-frequency amplitude, atime-frequency phase, a time-frequency trend, and/or a time-frequencycharacteristics of the wireless signal. The CI may be associated with adecomposition of the wireless signal. The CI may be associated withinformation associated with a direction, an angle of arrival (AoA), anangle of a directional antenna, and/or a phase of the wireless signalthrough the wireless multipath channel. The CI may be associated withattenuation patterns of the wireless signal through the wirelessmultipath channel. Each CI may be associated with a Type 1 device and aType 2 device. Each CI may be associated with an antenna of the Type 1device and an antenna of the Type 2 device.

The channel information (CI) may be obtained from a communicationhardware that is capable of providing the CI. The communication hardwaremay be a WiFi-capable chip/IC (integrated circuit), a chip compliantwith a 802.11 or 802.16 or another wireless/radio standard, a nextgeneration WiFi-capable chip, a LTE-capable chip, a 5G-capable chip, a6G/7G/8G-capable chip, a Bluetooth-enabled chip, a BLE (Bluetooth lowpower)-enabled chip, a UWB chip, another communication chip (e.g.Zigbee, WiMax, mesh network), etc. The communication hardware computesthe CI and stores the CI in a buffer memory and make the CI availablefor extraction. The CI may comprise data and/or at least one matricesrelated to channel state information (CSI). The at least one matricesmay be used for channel equalization, and/or beam forming, etc.

The wireless multipath channel may be associated with a venue. Theattenuation may be due to signal propagation in the venue, signalpropagating/reflection/refraction/diffraction through/at/around air(e.g. air of venue), refraction medium/reflection surface such as wall,doors, furniture, obstacles and/or barriers, etc. The attenuation may bedue to reflection at surfaces and obstacles (e.g. reflection surface,obstacle) such as floor, ceiling, furniture, fixtures, objects, people,pets, etc.

Each CI may be associated with a time stamp. Each CI may comprise N1components (e.g. N1 frequency domain components in CFR, N1 time domaincomponents in CIR, or N1 decomposition components). Each component maybe associated with a component index. Each component may be a realquantity, an imaginary quantity, a complex quantity, a magnitude, aphase, a flag, and/or a set. Each CI may comprise a vector of complexnumbers, a matrix of complex numbers, a set of mixed quantities, and/ora multi-dimensional collection of at least one complex numbers.

Components of a CI time series associated with a particular componentindex may form a respective component time series associated with therespective index. A CI time series may be divided into N1 component timeseries. Each respective component time series is associated with arespective component index. The characteristics/spatial-temporalinformation of the motion of the object may be monitored based on thecomponent time series.

A component-wise characteristics of a component-feature time series of aCI time series may be computed. The component-wise characteristics maybe a scalar (e.g. energy) or a function with a domain and a range (e.g.an autocorrelation function, a transform, an inverse transform). Thecharacteristics/spatial-temporal information of the motion of the objectmay be monitored based on the component-wise characteristics.

A total characteristics of the CI time series may be computed based onthe component-wise characteristics of each component time series of theCI time series. The total characteristics may be a weighted average ofthe component-wise characteristics. The characteristics/spatial-temporalinformation of the motion of the object may be monitored based on thetotal characteristics.

The Type 1 device and Type 2 device may support WiFi, WiMax, 3G/beyond3G, 4G/beyond 4G, LTE, 5G, 6G, 7G, Bluetooth, BLE, Zigbee, a proprietarywireless system, and/or another wireless system.

A common wireless system and/or a common wireless channel may be sharedby the Type 1 transceiver and/or the at least one Type 2 transceiver.The at least one Type 2 transceiver may transmit respective wirelesssignal contemporaneously using the common wireless system and/or thecommon wireless channel. The Type 1 transceiver may transmit a wirelesssignal to the at least one Type 2 transceiver using the common wirelesssystem and/or the common wireless channel.

A Type 1 device may temporarily function as a Type 2 device, and viceversa.

Each Type 1 device and Type 2 device may have at least onetransmitting/receiving antenna. Each CI may be associated with one ofthe transmitting antenna of the Type 1 device and one of the receivingantenna of the Type 2 device.

The at least one time series of CI may correspond to various antennapairs between the Type 1 device and the Type 2 device. The Type 1 devicemay have at least one antenna. The Type 2 device may also have at leastone antenna. Each time series of CI may be associated with an antenna ofthe Type 1 device and an antenna of the Type 2 device. Averaging orweighted averaging over antenna links may be performed. The averaging orweighted averaging may be over the at least one time series of CI. Theaveraging may optionally be performed on a subset of the at least onetime series of CI corresponding to a subset of the antenna pairs.

Time stamps of CI of a portion of a time series of CI may be irregularand may be corrected so that corrected timestamps of time-corrected CImay be uniformly spaced in time. In the case of multiple Type 1 devicesand/or multiple Type 2 devices, the corrected time stamp may be withrespect to the same or different clock.

An original timestamp associated with each of the CI may be determined.The original timestamp may not be uniformly spaced in time. Originaltimestamps of all CI of the particular portion of the particular timeseries of CI in the current sliding time window may be corrected so thatcorrected timestamps of time-corrected CI may be uniformly spaced intime.

The characteristics and/or spatial-temporal information may comprise: alocation, a position (e.g. initial position, new position), a positionon a map, a height, a horizontal location, a vertical location, adistance, a displacement, a speed, an acceleration, a rotational speed,a rotational acceleration, an angle of motion, direction of motion,rotation, a path, deformation, transformation, shrinking, expanding, agait, a gait cycle, a head motion, a repeated motion, a periodic motion,a pseudo-periodic motion, an impulsive motion, a sudden motion, afall-down motion, a transient motion, a behavior, a transient behavior,a period of motion, a frequency of motion, a time trend, a temporalprofile, a temporal characteristics, an occurrence, a change, a changein frequency, a change in timing, a change of gait cycle, a timing, astarting time, an ending time, a duration, a history of motion, a motiontype, a motion classification, a frequency, a frequency spectrum, afrequency characteristics, a presence, an absence, a proximity, anapproaching, a receding, an identity of the object, a composition of theobject, a head motion rate, a head motion direction, a mouth-relatedrate, an eye-related rate, a breathing rate, a heart rate, a hand motionrate, a hand motion direction, a leg motion, a body motion, a walkingrate, a hand motion rate, a positional characteristics, acharacteristics associated with movement of the object, a tool motion, amachine motion, a complex motion, and/or a combination of multiplemotions, an event and/or another information. The processor sharescomputational workload with the Type 1 heterogeneous wireless device andType 2 heterogeneous wireless device.

The Type 1 device and/or Type 2 device may be a local device. The localdevice may be: a smart phone, a smart device, TV, sound bar, set-topbox, access point, router, repeater, remote control, speaker, fan,refrigerator, microwave, oven, coffee machine, hot water pot, utensil,table, chair, light, lamp, door lock, camera, microphone, motion sensor,security device, fire hydrant, garage door, switch, power adapter,computer, dongle, computer peripheral, electronic pad, sofa, tile,accessory, smart home device, smart vehicle device, smart office device,smart building device, smart manufacturing device, smart watch, smartglasses, smart clock, smart television, smart oven, smart refrigerator,smart air-conditioner, smart chair, smart table, smart accessory, smartutility, smart appliance, smart machine, smart vehicle, aninternet-of-thing (IoT) device, an internet-enabled device, a computer,a portable computer, a tablet, a smart house, a smart office, a smartbuilding, a smart parking lot, a smart system, and/or another device.

Each Type 1 device may be associated with a respective identify (ID).Each Type 2 device may also be associated with a respective identify(ID). The ID may comprise: a numeral, a combination of text and numbers,a name, a password, an account, an account ID, a web link, a webaddress, index to some information, and/or another ID. The ID may beassigned. The ID may be assigned by hardware (e.g. hardwired, via adongle and/or other hardware), software and/or firmware. The ID may bestored (e.g. in a database, in memory, in a server, in the cloud, storedlocally, stored remotely, stored permanently, stored temporarily) andmay be retrieved. The ID may be associated with at least one record,account, user, household, address, phone number, social security number,customer number, another ID, time stamp, and/or collection of data. TheID and/or part of the ID of a Type 1 device may be made available to aType 2 device. The ID may be used for registration, initialization,communication, identification, verification, detection, recognition,authentication, access control, cloud access, networking, socialnetworking, logging, recording, cataloging, classification, tagging,association, pairing, transaction, electronic transaction, and/orintellectual property control, by the Type 1 device and/or the Type 2device.

The object may be a person, passenger, child, older person, baby,sleeping baby, baby in a vehicle, patient, worker, high-value worker,expert, specialist, waiter, customer in a mall, traveler inairport/train station/bus terminal/shipping terminals,staff/worker/customer service personnel in afactory/mall/supermarket/office/workplace, serviceman in sewage/airventilation system/lift well, lifts in lift wells, elevator, inmate,people to be tracked/monitored, animal, a plant, a living object, a pet,dog, cat, smart phone, phone accessory, computer, tablet, portablecomputer, dongle, computing accessory, networked devices, WiFi devices,IoT devices, smart watch, smart glasses, smart devices, speaker, keys,smart key, wallet, purse, handbag, backpack, goods, cargo, luggage,equipment, motor, machine, air conditioner, fan, air conditioningequipment, light fixture, moveable light, television, camera, audioand/or video equipment, stationary, surveillance equipment, parts,signage, tool, cart, ticket, parking ticket, toll ticket, airplaneticket, credit card, plastic card, access card, food packaging, utensil,table, chair, cleaning equipment/tool, vehicle, car, cars in parkingfacilities, merchandise in a warehouse/store/supermarket/distributioncenter, boat, bicycle, airplane, drone, remote control car/plane/boat,robot, manufacturing device, assembly line, material/unfinishedpart/robot/wagon/transports on a factory floor, object to be tracked inairport/shopping mart/supermarket, non-object, absence of an object,presence of an object, object with a form, object with a changing form,object with no form, mass of fluid, mass of liquid, mass of gas/smoke,fire, flame, electromagnetic (EM) source, EM medium, and/or anotherobject.

The object itself may be communicatively coupled with some network, suchas WiFi, MiFi, 3G/4G/LTE/5G, Bluetooth, BLE, WiMax, Zigbee, meshnetwork, adhoc network, and/or other network. The object itself may bebulky with AC power supply, but is moved during installation, cleaning,maintenance, renovation, etc. It may also be installed in a moveableplatform such as a lift, a pad, a movable, platform, an elevator, aconveyor belt, a robot, a drone, a forklift, a car, a boat, a vehicle,etc.

The object may have multiple parts, each part with different movement.For example, the object may be a person walking forward. While walking,his left hand and right hand may move in different direction, withdifferent instantaneous speed, acceleration, motion, etc.

The wireless transmitter (e.g. Type 1 device), the wireless receiver(e.g. Type 2 device), another wireless transmitter and/or anotherwireless receiver may move with the object and/or another object (e.g.in prior movement, current movement and/or future movement. They may becommunicatively coupled to one or more nearby device. They may transmittime series of CI and/or information associated with the time series ofCI to the nearby device, and/or each other. They may be with the nearbydevice.

The wireless transmitter and/or the wireless receiver may be part of asmall (e.g. coin-size, cigarette box size, or even smaller),light-weight portable device. The portable device may be wirelesslycoupled with a nearby device.

The nearby device may be smart phone, iPhone, Android phone, smartdevice, smart appliance, smart vehicle, smart gadget, smart TV, smartrefrigerator, smart speaker, smart watch, smart glasses, smart pad,iPad, computer, wearable computer, notebook computer, gateway. Thenearby device may be connected to a cloud server, a local server and/orother server via internet, wired internet connection and/or wirelessinternet connection. The nearby device may be portable.

The portable device, the nearby device, a local server and/or a cloudserver may share the computation and/or storage for a task (e.g. obtaintime series of CI, determine characteristics/spatial-temporalinformation of the object associated with the movement of the object,computation of time series of power information, determining/computingthe particular function, searching for local extremum, classification,identifying particular value of time offset, de-noising, processing,simplification, cleaning, wireless smart sensing task, extract CI fromwireless signal, switching, segmentation, estimate trajectory, processthe map, correction, corrective adjustment, adjustment, map-basedcorrection, detecting error, checking for boundary hitting,thresholding, etc.) and information (e.g. time series of CI).

The nearby device may/may not move with the object. The nearby devicemay be portable/not portable/moveable/non-moveable. The nearby devicemay use battery power, solar power, AC power and/or other power source.The nearby device may have replaceable/non-replaceable battery, and/orrechargeable/non-rechargeable battery. The nearby device may be similarto the object. The nearby device may have identical (and/or similar)hardware and/or software to the object. The nearby device may be a smartdevice, a network enabled device, a device with connection toWiFi/3G/4G/5G/6G/Zigbee/Bluetooth/adhoc network/other network, a smartspeaker, a smart watch, a smart clock, a smart appliance, a smartmachine, a smart equipment, a smart tool, a smart vehicle, aninternet-of-thing (IoT) device, an internet-enabled device, a computer,a portable computer, a tablet, and another device.

The nearby device and/or at least one processor associated with thewireless receiver, the wireless transmitter, the another wirelessreceiver, the another wireless transmitter and/or a cloud server (in thecloud) may determine the initial spatial-temporal information of theobject. Two or more of them may determine the initial spatial-temporalinfo jointly. Two or more of them may share intermediate information inthe determination of the initial spatial-temporal information (e.g.initial position).

In one example, the wireless transmitter (e.g. Type 1 device, or TrackerBot) may move with the object. The wireless transmitter may send thewireless signal to the wireless receiver (e.g. Type 2 device, or OriginRegister) or determining the initial spatial-temporal information (e.g.initial position) of the object. The wireless transmitter may also sendthe wireless signal and/or another wireless signal to another wirelessreceiver (e.g. another Type 2 device, or another Origin Register) forthe monitoring of the motion (spatial-temporal info) of the object. Thewireless receiver may also receive the wireless signal and/or anotherwireless signal from the wireless transmitter and/or the anotherwireless transmitter for monitoring the motion of the object. Thelocation of the wireless receiver and/or the another wireless receivermay be known.

In another example, the wireless receiver (e.g. Type 2 device, orTracker Bot) may move with the object. The wireless receiver may receivethe wireless signal transmitted from the wireless transmitter (e.g. Type1 device, or Origin Register) for determining the initialspatial-temporal info (e.g. initial position) of the object. Thewireless receiver may also receive the wireless signal and/or anotherwireless signal from another wireless transmitter (e.g. another Type 1device, or another Origin Register) for the monitoring of the currentmotion (e.g. spatial-temporal info) of the object. The wirelesstransmitter may also transmit the wireless signal and/or anotherwireless signal to the wireless receiver and/or the another wirelessreceiver (e.g. another Type 2 device, or another Tracker Bot) formonitoring the motion of the object. The location of the wirelesstransmitter and/or the another wireless transmitter may be known.

The venue may be a space such as a room, a house, an office, aworkplace, a hallway, a walkway, a lift, a lift well, an escalator, anelevator, a sewage system, air ventilations system, a staircase, agathering area, a duct, an air duct, a pipe, a tube, an enclosedstructure, a semi-enclosed structure, an enclosed area, an area with atleast one wall, a plant, a machine, an engine, a structure with wood, astructure with glass, a structure with metal, a structure with walls, astructure with doors, a structure with gaps, a structure with reflectionsurface, a structure with fluid, a building, a roof top, a store, afactory, an assembly line, a hotel room, a museum, a classroom, aschool, a university, a government building, a warehouse, a garage, amall, an airport, a train station, a bus terminal, a hub, atransportation hub, a shipping terminal, a government facility, a publicfacility, a school, a university, an entertainment facility, arecreational facility, a hospital, a seniors home, an elderly carefacility, a community center, a stadium, a playground, a park, a field,a sports facility, a swimming facility, a track and/or field, abasketball court, a tennis court, a soccer stadium, a baseball stadium,a gymnasium, a hall, a garage, a shopping mart, a mall, a supermarket, amanufacturing facility, a parking facility, a construction site, amining facility, a transportation facility, a highway, a road, a valley,a forest, a wood, a terrain, a landscape, a den, a patio, a land, apath, an amusement park, an urban area, a rural area, a suburban area, ametropolitan area, a garden, a square, a plaza, a music hall, a downtownfacility, an over-air facility, a semi-open facility, a closed area, atrain platform, a train station, a distribution center, a warehouse, astore, a distribution center, a storage facility, an undergroundfacility, a space (e.g. above ground, outer-space) facility, a floatingfacility, a cavern, a tunnel facility, an indoor facility, an open-airfacility, an outdoor facility with some walls/doors/reflective barriers,an open facility, a semi-open facility, a car, a truck, a bus, a van, acontainer, a ship/boat, a submersible, a train, a tram, an airplane, avehicle, a mobile home, a cave, a tunnel, a pipe, a channel, ametropolitan area, downtown area with relatively tall buildings, avalley, a well, a duct, a pathway, a gas line, an oil line, a waterpipe, a network of interconnectingpathways/alleys/roads/tubes/cavities/caves/pipe-like structure/airspace/fluid space, a human body, an animal body, a body cavity, anorgan, a bone, a teeth, a soft tissue, a hard tissue, a rigid tissue, anon-rigid tissue, a blood/body fluid vessel, windpipe, air duct, a den,etc. The venue may be an indoor space, an outdoor space, The venue mayinclude both the inside and outside of the space. For example, the venuemay include both the inside of a building and the outside of thebuilding. For example, the venue can be a building that has one floor ormultiple floors, and a portion of the building can be underground. Theshape of the building can be, e.g., round, square, rectangular,triangle, or irregular-shaped. These are merely examples. The disclosurecan be used to detect events in other types of venue or spaces.

The wireless transmitter (e.g. Type 1 device) and/or the wirelessreceiver (e.g. Type 2 device) may be embedded in a portable device (e.g.a module, or a device with the module) that may move with the object(e.g. in prior movement and/or current movement). The portable devicemay be communicatively coupled with the object using a wired connection(e.g. through USB, microUSB, Firewire, HDMI, serial port, parallel port,and other connectors) and/or a wireless connection (e.g. Bluetooth,Bluetooth Low Energy (BLE), WiFi, LTE, ZigBee, etc.). The portabledevice may be a lightweight device. The portable may be powered bybattery, rechargeable battery and/or AC power. The portable device maybe very small (e.g. at sub-millimeter scale and/or sub-centimeterscale), and/or small (e.g. coin-size, card-size, pocket-size, orlarger). The portable device may be large, sizable, and/or bulky (e.g.heavy machinery to be installed). The portable device may be a WiFihotspot, an access point, a mobile WiFi (MiFi), a dongle with USB/microUSB/Firewlire/other connector, a smartphone, a portable computer, acomputer, a tablet, a smart device, an internet-of-thing (IoT) device, aWiFi-enabled device, an LTE-enabled device, a smart watch, a smartglass, a smart mirror, a smart antenna, a smart battery, a smart light,a smart pen, a smart ring, a smart door, a smart window, a smart clock,a small battery, a smart wallet, a smart belt, a smart handbag, a smartclothing/garment, a smart ornament, a smart packaging, a smartpaper/book/magazine/poster/printed matter/signage/display/lightedsystem/lighting system, a smart key/tool, a smartbracelet/chain/necklace/wearable/accessory, a smart pad/cushion, a smarttile/block/brick/building material/other material, a smart garbagecan/waste container, a smart food carriage/storage, a smart ball/racket,a smart chair/sofa/bed, a smart shoe/footwear/carpet/mat/shoe rack, asmart glove/hand wear/ring/hand ware, a smarthat/headwear/makeup/sticker/tattoo, a smart mirror, a smart toy, a smartpill, a smart utensil, a smart bottle/food container, a smart tool, asmart device, an IoT device, a WiFi enabled device, a network enableddevice, a 3G/4G/5G/6G enabled device, an embeddable device, animplantable device, air conditioner, refrigerator, heater, furnace,furniture, oven, cooking device, television/set-top box (STB)/DVDplayer/audio player/video player/remote control, hi-fi, audio device,speaker, lamp/light, wall, door, window, roof, rooftile/shingle/structure/atticstructure/device/feature/installation/fixtures, lawn mower/gardentools/yard tools/mechanics tools/garage tools, garbage can/container,20-ft/40-ft container, storage container,factory/manufacturing/production device, repair tools, fluid container,machine, machinery to be installed, a vehicle, a cart, a wagon,warehouse vehicle, a car, a bicycle, a motorcycle, a boat, a vessel, anairplane, a basket/box/bag/bucket/container, a smartplate/cup/bowl/pot/mat/utensils/kitchen tools/kitchen devices/kitchenaccessories/cabinets/tables/chairs/tiles/lights/water pipes/taps/gasrange/oven/dishwashing machine/etc. The portable device may have abattery that may be replaceable, irreplaceable, rechargeable, and/ornon-rechargeable. The portable device may be wirelessly charged. Theportable device may be a smart payment card. The portable device may bea payment card used in parking lots, highways, entertainment parks, orother venues/facilities that need payment. The portable device may havean identity (ID) as described above.

An event may be monitored based on the time series of CI. The event maybe an object related event, such as fall-down of the object (e.g. anperson and/or a sick person), a rotation, a hesitation, a pause, animpact (e.g. a person hitting a sandbag, a door, a window, a bed, achair, a table, a desk, a cabinet, a box, another person, an animal, abird, a fly, a table, a chair, a ball, a bowling ball, a tennis ball, afootball, a soccer ball, a baseball, a basketball, a volley ball, etc.),a two-body action (e.g. a person letting go a balloon, catching a fish,molding a clay, writing a paper, a person typing on a computer, etc.), acar moving in a garage, a person carrying a smart phone and walkingaround an airport/mall/government building/office/etc, an autonomousmoveable object/machine moving around (e.g. vacuum cleaner, utilityvehicle, a car, drone, self-driving car, etc.).

The task or the wireless smart sensing task may comprise: objectdetection, presence detection, object recognition, object verification,tool detection, tool recognition, tool verification, machine detection,machine recognition, machine verification, human detection, humanrecognition, human verification, baby detection, baby recognition, babyverification, human breathing detection, motion detection, motionestimation, motion verification, periodic motion detection, periodicmotion estimation, periodic motion verification, stationary motiondetection, stationary motion estimation, stationary motion verification,cyclo-stationary motion detection, cyclo-stationary motion estimation,cyclo-stationary motion verification, transient motion detection,transient motion estimation, transient motion verification, trenddetection, trend estimation, trend verification, breathing detection,breathing estimation, breathing estimation, human biometrics detection,human biometrics estimation, human biometrics verification, environmentinformatics detection, environment informatics estimation, environmentinformatics verification, gait detection, gait estimation, gaitverification, gesture detection, gesture estimation, gestureverification, machine learning, supervised learning, unsupervisedlearning, semi-supervised learning, clustering, feature extraction,featuring training, principal component analysis, eigen-decomposition,frequency decomposition, time decomposition, time-frequencydecomposition, functional decomposition, other decomposition, training,discriminative training, supervised training, unsupervised training,semi-supervised training, neural network, sudden motion detection,fall-down detection, danger detection, life-threat detection, regularmotion detection, stationary motion detection, cyclo-stationary motiondetection, intrusion detection, suspicious motion detection, security,safety monitoring, navigation, guidance, map-based processing, map-basedcorrection, irregularity detection, locationing, tracking, multipleobject tracking, indoor tracking, indoor position, indoor navigation,power transfer, wireless power transfer, object counting, car trackingin parking garage, patient detection, patient monitoring, patientverification, wireless communication, data communication, signalbroadcasting, networking, coordination, administration, encryption,protection, cloud computing, other processing and/or other task. Thetask may be performed by the Type 1 device, the Type 2 device, anotherType 1 device, another Type 2 device, a nearby device, a local server,an edge server, a cloud server, and/or another device.

A first part of the task may comprise at least one of: preprocessing,signal conditioning, signal processing, post-processing, denoising,feature extraction, coding, encryption, transformation, mapping, motiondetection, motion estimation, motion change detection, motion patterndetection, motion pattern estimation, motion pattern recognition, vitalsign detection, vital sign estimation, vital sign recognition, periodicmotion detection, periodic motion estimation, breathing rate detection,breathing rate estimation, breathing pattern detection, breathingpattern estimation, breathing pattern recognition, heart beat detection,heart beat estimation, heart pattern detection, heart patternestimation, heart pattern recognition, gesture detection, gestureestimation, gesture recognition, speed detection, speed estimation,object locationing, object tracking, navigation, accelerationestimation, acceleration detection, fall-down detection, changedetection, intruder detection, baby detection, baby monitoring, patientmonitoring, object recognition, wireless power transfer, and/or wirelesscharging.

A second part of the task may comprise at least one of: a smart hometask, a smart office task, a smart building task, a smart factory task(e.g. manufacturing using a machine or an assembly line), a smartinternet-of-thing (IoT) task, a smart system task, a smart homeoperation, a smart office operation, a smart building operation, a smartmanufacturing operation (e.g. moving supplies/parts/raw material to amachine/an assembly line), an IoT operation, a smart system operation,turning on a light, turning off the light, controlling the light in atleast one of: a room, a region, and/or the venue, playing a sound clip,playing the sound clip in at least one of: the room, the region, and/orthe venue, playing the sound clip of at least one of: a welcome, agreeting, a farewell, a first message, and/or a second messageassociated with the first part of the task, turning on an appliance,turning off the appliance, controlling the appliance in at least one of:the room, the region, and/or the venue, turning on an electrical system,turning off the electrical system, controlling the electrical system inat least one of: the room, the region, and/or the venue, turning on asecurity system, turning off the security system, controlling thesecurity system in at least one of: the room, the region, and/or thevenue, turning on a mechanical system, turning off a mechanical system,controlling the mechanical system in at least one of: the room, theregion, and/or the venue, and/or controlling at least one of: an airconditioning system, a heating system, a ventilation system, a lightingsystem, a heating device, a stove, an entertainment system, a door, afence, a window, a garage, a computer system, a networked device, anetworked system, a home appliance, an office equipment, a lightingdevice, a robot (e.g. robotic arm), a smart vehicle, a smart machine, anassembly line, a smart device, an internet-of-thing (IoT) device, asmart home device, and/or a smart office device.

The task may include: detect a user returning home, detect a userleaving home, detect a user moving from one room to another,detect/control/lock/unlock/open/close/partially open awindow/door/garage door/blind/curtain/panel/solar panel/sun shade,detect a pet, detect/monitor a user doing something (e.g. sleeping onsofa, sleeping in bedroom, running on treadmill, cooking, sitting onsofa, watching TV, eating in kitchen, eating in dining room, goingupstairs/downstairs, going outside/coming back, in the rest room, etc.),monitor/detect location of a user/pet, do something automatically upondetection, do something for the user automatically upon detecting theuser, turn on/off/dim a light, turn on/off music/radio/homeentertainment system, turn on/off/adjust/control TV/HiFi/set-top-box(STB)/home entertainment system/smart speaker/smart device, turnon/off/adjust air conditioning system, turn on/off/adjust ventilationsystem, turn on/off/adjust heating system, adjust/control curtains/lightshades, turn on/off/wake a computer, turn on/off/pre-heat/control coffeemachine/hot water pot, turn on/off/control/preheat cooker/oven/microwaveoven/another cooking device, check/adjust temperature, check weatherforecast, check telephone message box, check mail, do a system check,control/adjust a system, check/control/arm/disarm security system/babymonitor, check/control refrigerator, give a report (e.g. through aspeaker such as Google home, Amazon Echo, on a display/screen, via awebpage/email/messaging system/notification system, etc.).

For example, when a user arrives home in his car, the task may be to,automatically, detect the user or his car approaching, open the garagedoor upon detection, turn on the driveway/garage light as the userapproaches the garage, turn on air conditioner/heater/fan, etc. As theuser enters the house, the task may be to, automatically, turn on theentrance light, turn off driveway/garage light, play a greeting messageto welcome the user, turn on the music, turn on the radio and tuning tothe user's favorite radio news channel, open the curtain/blind, monitorthe user's mood, adjust the lighting and sound environment according tothe user's mood or the current/imminent event (e.g. do romantic lightingand music because the user is scheduled to eat dinner with girlfriend in1 hour) on the user's daily calendar, warm the food in microwave thatthe user prepared in the morning, do a diagnostic check of all systemsin the house, check weather forecast for tomorrow's work, check news ofinterest to the user, check user's calendar and to-do list and playreminder, check telephone answer system/messaging system/email and givea verbal report using dialog system/speech synthesis, remind (e.g. usingaudible tool such as speakers/HiFi/speechsynthesis/sound/voice/music/song/sound field/background soundfield/dialog system, using visual tool such as TV/entertainmentsystem/computer/notebook/smartpad/display/light/color/brightness/patterns/symbols, using haptictool/virtual reality tool/gesture/tool, using a smartdevice/appliance/material/furniture/fixture, using web tool/server/cloudserver/fog server/edge server/home network/mesh network, using messagingtool/notification tool/communication tool/scheduling tool/email, usinguser interface/GUI, using scent/smell/fragrance/taste, using neuraltool/nervous system tool, using a combination, etc.) the user of hismother's birthday and to call her, prepare a report, and give the report(e.g. using a tool for reminding as discussed above). The task may turnon the air conditioner/heater/ventilation system in advance, or adjusttemperature setting of smart thermostat in advance, etc. As the usermoves from the entrance to the living room, the task may be to turn onthe living room light, open the living room curtain, open the window,turn off the entrance light behind the user, turn on the TV and set-topbox, set TV to the user's favorite channel, adjust an applianceaccording to the user's preference and conditions/states (e.g. adjustlighting and choose/play music to build a romantic atmosphere), etc.

Another example may be: When the user wakes up in the morning, the taskmay be to detect the user moving around in the bedroom, open theblind/curtain, open the window, turn off the alarm clock, adjust indoortemperature from night-time temperature profile to day-time temperatureprofile, turn on the bedroom light, turn on the restroom light as theuser approaches the restroom, check radio or streaming channel and playmorning news, turn on the coffee machine and preheat the water, turn offsecurity system, etc. When the user walks from bedroom to kitchen, thetask may be to turn on the kitchen and hallway lights, turn off thebedroom and restroom lights, move the music/message/reminder from thebedroom to the kitchen, turn on the kitchen TV, change TV to morningnews channel, lower the kitchen blind and open the kitchen window tobring in fresh air, unlock backdoor for the user to check the backyard,adjust temperature setting for the kitchen, etc.

Another example may be: When the user leaves home for work, the task maybe to detect the user leaving, play a farewell and/or have-a-good-daymessage, open/close garage door, turn on/off garage light and drivewaylight, turn off/dim lights to save energy (just in case the userforgets), close/lock all windows/doors (just in case the user forgets),turn off appliance (especially stove, oven, microwave oven), turn on/armthe home security system to guard the home against any intruder, adjustair conditioning/heating/ventilation systems to “away-from-home” profileto save energy, send alerts/reports/updates to the user's smart phone,etc.

A motion may comprise at least one of: a no-motion, a resting motion, anon-moving motion, a deterministic motion, a transient motion, afall-down motion, a repeating motion, a periodic motion, apseudo-periodic motion, a periodic motion associated with breathing, aperiodic motion associated with heartbeat, a periodic motion associatedwith a living object, a periodic motion associated with a machine, aperiodic motion associated with a man-made object, a periodic motionassociated with nature, a complex motion with a transient element and aperiodic element, a repetitive motion, a non-deterministic motion, aprobabilistic motion, a chaotic motion, a random motion, a complexmotion with a non-deterministic element and a deterministic element, astationary random motion, a pseudo-stationary random motion, acyclo-stationary random motion, a non-stationary random motion, astationary random motion with a periodic autocorrelation function (ACF),a random motion with a periodic ACF for a period of time, a randommotion that is pseudo-stationary for a period of time, a random motionof which an instantaneous ACF has a pseudo-periodic element for a periodof time, a machine motion, a mechanical motion, a vehicle motion, adrone motion, an air-related motion, a wind-related motion, aweather-related motion, a water-related motion, a fluid-related motion,an ground-related motion, a change in electro-magnetic characteristics,a sub-surface motion, a seismic motion, a plant motion, an animalmotion, a human motion, a normal motion, an abnormal motion, a dangerousmotion, a warning motion, a suspicious motion, a rain, a fire, a flood,a tsunami, an explosion, a collision, an imminent collision, a humanbody motion, a head motion, a facial motion, an eye motion, a mouthmotion, a tongue motion, a neck motion, a finger motion, a hand motion,an arm motion, a shoulder motion, a body motion, a chest motion, anabdominal motion, a hip motion, a leg motion, a foot motion, a bodyjoint motion, a knee motion, an elbow motion, an upper body motion, alower body motion, a skin motion, a below-skin motion, a subcutaneoustissue motion, a blood vessel motion, an intravenous motion, an organmotion, a heart motion, a lung motion, a stomach motion, an intestinemotion, a bowel motion, an eating motion, a breathing motion, a facialexpression, an eye expression, a mouth expression, a talking motion, asinging motion, an eating motion, a gesture, a hand gesture, an armgesture, a keystroke, a typing stroke, a user-interface gesture, aman-machine interaction, a gait, a dancing movement, a coordinatedmovement, and/or a coordinated body movement.

The heterogeneous IC of the Type 1 device and/or any Type 2 receiver maycomprise a low-noise amplifier (LNA), a power amplifier, atransmit-receive switch, a media access controller, a baseband radio, a2.4 GHz radio, a 3.65 GHz radio, a 4.9 GHz radio, a 5 GHz radio, a 5.9GHz radio, a below 6 GHz radio, a below 60 GHz radio and/or anotherradio.

The heterogeneous IC may comprise a processor, a memory communicativelycoupled with the processor, and a set of instructions stored in thememory to be executed by the processor. The IC may be an applicationspecific integrated circuit (ASIC), a field programmable gate array(FPGA), other programmable logic device, discrete logic, and/or acombination.

The heterogeneous IC may support a broadband network, a wirelessnetwork, a mobile network, a mesh network, a cellular network, awireless local area network (WLAN), a wide area network (WAN), and ametropolitan area network (MAN), a WLAN standard, WiFi, LTE, a 802.11standard, 802.11a, 802.11b, 802.11g, 802.11n, 802.11ac, 802.11ad,802.11af, 802.11ah, 802.11ax, 802.11ay, a mesh network standard, a802.15 standard, a 802.16 standard, a cellular network standard, 3G,3.5G, 4G, beyond 4G, 4.5G, 5G, 6G, 7G, 8G, 9G, Bluetooth, BluetoothLow-Energy (BLE), Zigbee, WiMax, and/or another wireless networkprotocol.

The processor may comprise a general purpose processor, a specialpurpose processor, a microprocessor, a microcontroller, an embeddedprocessor, a digital signal processor, a central processing unit (CPU),a graphical processing unit (GPU), a multi-processor, a multi-coreprocessor, and/or a processor with graphics capability, and/or acombination.

The memory may be volatile, non-volatile, random access memory (RAM),Read Only Memory (ROM), Electrically Programmable ROM (EPROM),Electrically Erasable Programmable ROM (EEPROM), hard disk, flashmemory, CD-ROM, DVD-ROM, a magnetic storage, an optical storage, anorganic storage, a storage system, a storage network, network storage,cloud storage, or other form of non-transitory storage medium known inthe art.

The set of instructions (machine executable code) corresponding to themethod steps may be embodied directly in hardware, in software, infirmware, or in combinations thereof.

The presentation may be a presentation in an audio-visual way, agraphical way (e.g. using GUI), a textual way, a symbolic way or amechanical way.

Computational workload associated with the method is shared among theprocessor, the Type 1 heterogeneous wireless device, the Type 2heterogeneous wireless device, a local server, a cloud server, andanother processor.

An operation, a pre-processing, a processing and/or a postprocessing maybe applied to data (e.g. time series of CI, autocorrelation). Anoperation may be preprocessing, processing and/or postprocessing. Thepreprocessing, processing and/or postprocessing may be an operation. Anoperation may comprise preprocessing, processing, post-processing,computing a function of the operands, filtering, linear filtering,nonlinear filtering, folding, grouping, energy computation, lowpassfiltering, bandpass filtering, highpass filtering, median filtering,rank filtering, quartile filtering, percentile filtering, modefiltering, finite impulse response (FIR) filtering, infinite impulseresponse (IIR) filtering, moving average (MA) filtering, autoregressive(AR) filtering, autoregressive moving averaging (ARMA) filtering,selective filtering, adaptive filtering, interpolation, decimation,subsampling, upsampling, resampling, time correction, time basecorrection, phase correction, magnitude correction, phase cleaning,magnitude cleaning, matched filtering, enhancement, restoration,denoising, smoothing, signal conditioning, enhancement, restoration,spectral analysis, linear transform, nonlinear transform, frequencytransform, inverse frequency transform, Fourier transform, wavelettransform, Laplace transform, Hilbert transform, Hadamard transform,trigonometric transform, sine transform, cosine transform, power-of-2transform, sparse transform, graph-based transform, graph signalprocessing, fast transform, a transform combined with zero padding,cyclic padding, padding, zero padding, feature extraction,decomposition, projection, orthogonal projection, non-orthogonalprojection, over-complete projection, eigen-decomposition, singularvalue decomposition (SVD), principle component analysis (PCA),independent component analysis (ICA), grouping, sorting, thresholding,soft thresholding, hard thresholding, clipping, soft clipping, firstderivative, second order derivative, high order derivative, convolution,multiplication, division, addition, subtraction, integration,maximization, minimization, local maximization, local minimization,optimization of a cost function, neural network, recognition, labeling,training, clustering, machine learning, supervised learning,unsupervised learning, semi-supervised learning, comparison with anothertime series of CI, similarity score computation, quantization, vectorquantization, matching pursuit, compression, encryption, coding,storing, transmitting, normalization, temporal normalization, frequencydomain normalization, classification, clustering, labeling, tagging,learning, detection, estimation, learning network, mapping, remapping,expansion, storing, retrieving, transmitting, receiving, representing,merging, combining, splitting, tracking, monitoring, matched filtering,Kalman filtering, particle filter, intrapolation, extrapolation,importance sampling, Monte Carlo sampling, compressive sensing,representing, merging, combining, splitting, scrambling, errorprotection, forward error correction, doing nothing, time varyingprocessing, conditioning averaging, weighted averaging, arithmetic mean,geometric mean, averaging over selected frequency, averaging overantenna links, a logical operation, permutation, combination, sorting,AND, OR, XOR, union, intersection, vector addition, vector subtraction,vector multiplication, vector division, inverse, a norm, a distance,and/or another operation. The operation may be the preprocessing,processing, and/or post-processing. Operations may be applied jointly onmultiple time series or functions.

The function (e.g. function of the operands) may comprise: a scalarfunction, a vector function, a discrete function, a continuous function,a polynomial function, a magnitude, a phase, an exponential function, alogarithmic function, a trigonometric function, a transcendentalfunction, a logical function, a linear function, an algebraic function,a nonlinear function, a piecewise linear function, a real function, acomplex function, a vector-valued function, an inverse function, aderivative of a function, an integration of a function, a circularfunction, a function of another function, a one-to-one function, aone-to-many function, a many-to-one function, a many-to-many function, azero crossing, absolute function, indicator function, a mean, a mode, amedian, a range, a statistics, a variance, a trimmed mean, a percentile,a square, a cube, a root, a power, a sine, a cosine, a tangent, acotangent, a secant, a cosecant, an elliptical function, a parabolicfunction, a hyperbolic function, a game function, a zeta function, anabsolute value, a thresholding, a quantization, a piecewise constantfunction, a composite function, a function of function, an input timefunction processed with an operation (e.g. filtering), a probabilisticfunction, a stochastic function, a random function, an ergodic function,a stationary function, a deterministic function, a transformation, afrequency transform, an inverse frequency transform, a discrete timetransform, Laplace transform, Hilbert transform, sine transform, cosinetransform, triangular transform, wavelet transform, integer transform,power-of-2 transform, sparse transform, projection, decomposition,principle component analysis (PCA), independent component analysis(ICA), neural network, feature extraction, a moving function, a functionof a moving window of neighboring items of a time series, a filteringfunction, a convolution, a mean function, a variance function, astatistical function, short-time transform, discrete transform, discreteFourier transform, discrete cosine transform, discrete sine transform,Hadamard transform, eigen-decomposition, eigenvalue, singular valuedecomposition (SVD), singular value, orthogonal decomposition, matchingpursuit, sparse transform, sparse approximation, any decomposition,graph-based processing, graph-based transform, graph signal processing,classification, labeling, learning, machine learning, detection,estimation, feature extraction, learning network, feature extraction,denoising, signal enhancement, coding, encryption, mapping, remapping,vector quantization, lowpass filtering, highpass filtering, bandpassfiltering, matched filtering, Kalman filtering, preprocessing,postprocessing, particle filter, FIR filtering, IIR filtering,autoregressive (AR) filtering, adaptive filtering, first orderderivative, high order derivative, integration, zero crossing,smoothing, median filtering, mode filtering, sampling, random sampling,resampling function, downsampling, upsampling, interpolation,extrapolation, importance sampling, Monte Carlo sampling, compressivesensing, statistics, short term statistics, long term statistics, mean,variance, autocorrelation function, cross correlation, moment generatingfunction, time averaging, etc.

Machine learn, training, discriminative training, deep learning, neuralnetwork, continuous time processing, distributed computing, distributedstorage, acceleration usingGPU/DSP/coprocessor/multicore/multiprocessing may be applied to a step(or each step) of this disclosure.

A frequency transform may include Fourier transform, Laplace transform,Hadamard transform, Hilbert transform, sine transform, cosine transform,triangular transform, wavelet transform, integer transform, power-of-2transform, combined zero padding and transform, Fourier transform withzero padding, and/or another transform. Fast versions and/orapproximated versions of the transform may be performed. The transformmay be performed using floating point, and/or fixed point arithmetic.

An inverse frequency transform may include inverse Fourier transform,inverse Laplace transform, inverse Hadamard transform, inverse Hilberttransform, inverse sine transform, inverse cosine transform, inversetriangular transform, inverse wavelet transform, inverse integertransform, inverse power-of-2 transform, combined zero padding andtransform, inverse Fourier transform with zero padding, and/or anothertransform. Fast versions and/or approximated versions of the transformmay be performed. The transform may be performed using floating point,and/or fixed point arithmetic.

Sliding time window may have time varying window width. It may besmaller at the beginning to enable fast acquisition and may increaseover time to a steady-state size. The steady-state size may be relatedto the frequency, repeated motion, transient motion, and/orspatial-temporal information to be monitored. Even in steady state, thewindow size may be adaptively changed based on battery life, powerconsumption, available computing power, a change in amount of targets,the nature of motion to be monitored, etc.

The time shift between two sliding time windows at adjacent timeinstance may be constant/variable/locally adaptive over time. Whenshorter time shift is used, the update of any monitoring may be morefrequent which may be used for fast changing situations, object motions,and/or objects. Longer time shift may be used for slower situations,object motions, and/or objects.

The window width/size and/or time shift may be changed upon a userrequest/choice. The time shift may be changed automatically (e.g. ascontrolled by processor/computer/server/cloud server) and/or adaptively.

At least one characteristics of a function (e.g. auto-correlationfunction, auto-covariance function, cross-correlation function,cross-covariance function, power spectral density, a time function, afrequency domain function, a frequency transform) may be determined(e.g. by an object tracking server, the processor, the Type 1heterogeneous device, the Type 2 heterogeneous device, and/or anotherdevice). The at least one characteristics of the function may include: alocal maximum, a local minimum, a local extremum, a local extremum withpositive time offset, a first local extremum with positive time offset,an n^th local extremum with positive time offset, a local extremum withnegative time offset, a first local extremum with negative time offset,an n^th local extremum with negative time offset, a constrained (withargument within a constraint) maximum, minimum, constrained maximum,constrained minimum, a constrained extremum, a slope, a derivative, ahigher order derivative, a maximum slope, a minimum slope, a localmaximum slope, a local maximum slope with positive time offset, a localminimum slope, a constrained maximum slope, a constrained minimum slope,a maximum higher order derivative, a minimum higher order derivative, aconstrained higher order derivative, a zero-crossing, a zero crossingwith positive time offset, an n^th zero crossing with positive timeoffset, a zero crossing with negative time offset, an n^th zero crossingwith negative time offset, a constrained zero-crossing, a zero-crossingof slope, a zero-crossing of higher order derivative, and/or anothercharacteristics. At least one argument of the function associated withthe at least one characteristics of the function may be identified. Somequantity (e.g. a spatial-temporal information of the object) may bedetermined based on the at least one argument of the function.

A characteristics of a motion of an object in the venue may comprise atleast one of: an instantaneous characteristics, a short-termcharacteristics, repetitive characteristics, a recurringcharacteristics, a history, an incremental characteristics, a changingcharacteristics, a deviational characteristics, a phase, a magnitude, atime characteristics, a frequency characteristics, a time-frequencycharacteristics, a decomposition characteristics, an orthogonaldecomposition characteristics, a non-orthogonal decompositioncharacteristics, a deterministic characteristics, a probabilisticcharacteristics, a stochastic characteristics, an autocorrelationfunction (ACF), a mean, a variance, a statistics, a duration, a timing,a trend, a periodic characteristics, a long-term characteristics, ahistorical characteristics, an average characteristics, a currentcharacteristics, a past characteristics, a future characteristics, apredicted characteristics, a location, a distance, a height, a speed, adirection, a velocity, an acceleration, a change of the acceleration, anangle, an angular speed, an angular velocity, an angular acceleration ofthe object, a change of the angular acceleration, an orientation of theobject, an angular of a rotation, a deformation of the object, a shapeof the object, a change of shape of the object, a change of size of theobject, a change of structure of the object, and/or a change ofcharacteristics of the object.

At least one local maximum and at least one local minimum of thefunction may be identified. At least one localsignal-to-noise-ratio-like (SNR-like) parameter may be computed for eachpair of adjacent local maximum and local minimum. The SNR-like parametermay be a function (e.g. linear, log, exponential function, a monotonicfunction) of a fraction of a quantity (e.g. power, magnitude, etc.) ofthe local maximum over the same quantity of the local minimum. It mayalso be the function of a difference between the quantity of the localmaximum and the same quantity of the local minimum.

Significant local peaks may be identified or selected. Each significantlocal peak may be a local maximum with SNR-like parameter greater than athreshold T1 and/or a local maximum with amplitude greater than athreshold T2.

The at least one local minimum and the at least one local minimum in thefrequency domain may be identified/computed using a persistence-basedapproach.

A set of selected significant local peaks may be selected from the setof identified significant local peaks based on a selection criterion.The characteristics/spatial-temporal information of the object may becomputed based on the set of selected significant local peaks andfrequency values associated with the set of selected significant localpeaks.

In one example, the selection criterion may always correspond to selectthe strongest peaks in a range. While the strongest peaks may beselected, the unselected peaks may still be significant (rather strong).

Unselected significant peaks may be stored and/or monitored as“reserved” peaks for use in future selection in future sliding timewindows. As an example, there may be a particular peak (at a particularfrequency) appearing consistently over time. Initially, it may besignificant but not selected (as other peaks may be stronger). But inlater time, the peak may become stronger and more dominant and may beselected. When it became “selected”, it may be back-traced in time andmade “selected” in the earlier time when it was significant but notselected. In such case, the back-traced peak may replace a previouslyselected peak in an early time. The replaced peak may be the relativelyweakest, or a peak that appear in isolation in time (i.e. appearing onlybriefly in time).

In another example, the selection criterion may not correspond to selectthe strongest peaks in the range. Instead, it may consider not only the“strength” of the peak, but the “trace” of the peak—peaks that may havehappened in the past, especially those peaks that have been identifiedfor a long time.

For example, if a finite state machine (FSM) is used, it may select thepeak(s) based on the state of the FSM. Decision thresholds may becomputed adaptively based on the state of the FSM.

A similarity score may be computed (e.g. by a server, the processor, theType 1 device, the Type 2 device, a local server, a cloud server, and/oranother device) based on a pair of temporally adjacent CI of a timeseries of CI. The pair may come from the same sliding window or twodifferent sliding windows. The similarity score may also be based on apair of, temporally adjacent or not so adjacent, CI from two differenttime series of CI. The similarity score may be or may include: a timereversal resonating strength (TRRS), a correlation, a cross-correlation,an auto-correlation, a covariance, a cross-covariance, anauto-covariance, an inner product of two vectors, a distance score, adiscrimination score, a metric, a neural network output, a deep learningnetwork output, and/or another score. The characteristics and/orspatial-temporal information may be determined/computed based on thesimilarity score.

Any threshold may be pre-determined, adaptively determined and/ordetermined by a finite state machine. The adaptive determination may bebased on time, space, location, antenna, path, link, state, batterylife, remaining battery life, available power, available computationalresources, available network bandwidth, etc.

A threshold to be applied to a test statistics to differentiate twoevents (or two conditions, or two situations, or two states), A and B,may be determined. Data (e.g. CI, channel state information (CSI)) maybe collected under A and/or under B in a training situation. The teststatistics may be computed based on the data. Distributions of the teststatistics under A may be compared with distributions of the teststatistics under B, and the threshold may be chosen according to somecriteria. The criteria may comprise: maximum likelihood (ML), maximumaposterior probability (MAP), discriminative training, minimum type 1error for a given type 2 error, minimum type 2 error for a given type 1error, and/or other criteria. The threshold may be adjusted to achievedifferent sensitivity to the A, B and/or anotherevent/condition/situation/state. The threshold adjustment may beautomatic, semi-automatic and/or manual. The threshold adjustment may beapplied once, sometimes, often, periodically, occasionally,sporadically, and/or on demand. The threshold adjustment may beadaptive. The threshold adjustment may depend on the object, an objectmovement/location/direction/action, an objectcharacteristics/spatial-temporalinformation/size/property/trait/habit/behavior, the venue, afeature/fixture/furniture/barrier/material/machine/livingthing/thing/object/boundary/surface/medium that is in/at/of the venue, amap, a constraint of the map, the event/state/situation/condition, atime, a timing, a duration, a current state, a past history, a user,and/or a personal preference, etc.

A stopping criterion of an iterative algorithm may be that change of acurrent parameter (e.g. offset value) in the updating in an iteration isless than a threshold. The threshold may be 0.5, 1, 1.5, 2, or anothernumber. The threshold may be adaptive. It may change as the iterationprogresses. For the offset value, the adaptive threshold may bedetermined based on the task, particular value of the first time, thecurrent time offset value, the regression window, the regressionanalysis, the regression function, the regression error, the convexityof the regression function, and/or an iteration number.

The local extremum may be determined as the corresponding extremum ofthe regression function in the regression window. The local extremum maybe determined based on a set of time offset values in the regressionwindow and a set of associated regression function values. Each of theset of associated regression function values associated with the set oftime offset values may be within a range from the corresponding extremumof the regression function in the regression window.

The searching for a local extremum may comprise a robust search, aminimization, a maximization, an optimization, a statisticaloptimization, a dual optimization, a constraint optimization, a convexoptimization, a global optimization, a local optimization an energyminimization, a linear regression, a quadratic regression, a higherorder regression, a linear programming, a nonlinear programming, astochastic programming, a combinatorial optimization, a constraintprogramming, a constraint satisfaction, a calculus of variations, anoptimal control, a dynamic programming, a mathematical programming, amulti-objective optimization, a multi-modal optimization, a disjunctiveprogramming, a space mapping, an infinite-dimensional optimization, aheuristics, a metaheuristics, a convex programming, a semidefiniteprogramming, a conic programming, a cone programming, an integerprogramming, a quadratic programming, a fractional programming, anumerical analysis, a simplex algorithm, an iterative method, a gradientdescent, a subgradient method, a coordinate descent, a conjugategradient method, a Newton's algorithm, a sequential quadraticprogramming, an interior point method, an ellipsoid method, a reducedgradient method, a quasi-Newton method, a simultaneous perturbationstochastic approximation, an interpolation method, a pattern searchmethod, a line search, a non-differentiable optimization, a geneticalgorithm, an evolutionary algorithm, a dynamic relaxation, a hillclimbing, a particle swarm optimization, a gravitation search algorithm,a simulated annealing, a memetic algorithm, a differential evolution, adynamic relaxation, a stochastic tunneling, a Tabu search, a reactivesearch optimization, a curve fitting, a least square, a simulation basedoptimization, a variational calculus, and/or a variant. The search for alocal extremum may be associated with an objective function, a lossfunction, a cost function, a utility function, a fitness function, anenergy function, and/or an energy function.

Regression may be performed using a regression function to fit sampleddata (e.g. CI, a feature of CI, a component of CI) or another function(e.g. autocorrelation function) in a regression window. In at least oneiteration, a length of the regression window and/or a location of theregression window may change. The regression function may be a linearfunction, a quadratic function, a cubic function, a polynomial function,and/or another function.

The regression analysis may minimize an absolute error, a square error,a higher order error (e.g. third order, fourth order, etc.), a robusterror (e.g. square error for smaller error magnitude and absolute errorfor larger error magnitude, or a first kind of error for smaller errormagnitude and a second kind of error for larger error magnitude),another error, a weighted sum of absolute error (e.g. for a wirelesstransmitter with multiple antennas and a wireless receiver with multipleantennas, each pair of transmitter antenna and receiver antenna form alink. Error associated with different links may have different weights.One possibility is that some links and/or some components with largernoise may have smaller or bigger weight.), a weighted sum of squareerror, a weighted sum of higher order error, a weighted sum of robusterror, a weighted sum of the another error, an absolute cost, a squarecost, a higher order cost, a robust cost, another cost, a weighted sumof absolute cost, a weighted sum of square cost, a weighted sum ofhigher order cost, a weighted sum of robust cost, and/or a weighted sumof another cost.

The regression error determined may be an absolute error, a squareerror, a higher order error, a robust error, yet another error, aweighted sum of absolute error, a weighted sum of square error, aweighted sum of higher order error, a weighted sum of robust error,and/or a weighted sum of the yet another error.

The time offset associated with maximum regression error (or minimumregression error) of the regression function with respect to theparticular function in the regression window may become the updatedcurrent time offset in the iteration.

A local extremum may be searched based on a quantity comprising adifference of two different errors (e.g. a difference between absoluteerror and square error). Each of the two different errors may comprisean absolute error, a square error, a higher order error, a robust error,another error, a weighted sum of absolute error, a weighted sum ofsquare error, a weighted sum of higher order error, a weighted sum ofrobust error, and/or a weighted sum of the another error.

The quantity may be compared with an F-distribution, a centralF-distribution, another statistical distribution, a threshold, athreshold associated with a probability, a threshold associated with aprobability of finding a false peak, a threshold associated with theF-distribution, a threshold associated the central F-distribution,and/or a threshold associated with the another statistical distribution.

The regression window may be determined based on at least one of: themovement of the object, a quantity associated with the object, the atleast one characteristics and/or spatial-temporal information of theobject associated with the movement of the object, an estimated locationof the local extremum, a noise characteristics, an estimated noisecharacteristics, an F-distribution, a central F-distribution, anotherstatistical distribution, a threshold, a preset threshold, a thresholdassociated with a probability, a threshold associated with a desiredprobability, a threshold associated with a probability of finding afalse peak, a threshold associated with the F-distribution, a thresholdassociated the central F-distribution, a threshold associated with theanother statistical distribution, a condition that a quantity at thewindow center is largest within the regression window, a condition thatthe quantity at the window center is largest within the regressionwindow, a condition that there is only one of the local extremum of theparticular function for the particular value of the first time in theregression window, another regression window, and/or another condition.

The width of the regression window may be determined based on theparticular local extremum to be searched. The local extremum maycomprise first local maximum, second local maximum, higher order localmaximum, first local maximum with positive time offset value, secondlocal maximum with positive time offset value, higher local maximum withpositive time offset value, first local maximum with negative timeoffset value, second local maximum with negative time offset value,higher local maximum with negative time offset value, first localminimum, second local minimum, higher local minimum, first local minimumwith positive time offset value, second local minimum with positive timeoffset value, higher local minimum with positive time offset value,first local minimum with negative time offset value, second localminimum with negative time offset value, higher local minimum withnegative time offset value, first local extremum, second local extremum,higher local extremum, first local extremum with positive time offsetvalue, second local extremum with positive time offset value, higherlocal extremum with positive time offset value, first local extremumwith negative time offset value, second local extremum with negativetime offset value, and/or higher local extremum with negative timeoffset value.

A current parameter (e.g. time offset value) may be initialized based ona target value, a target profile, a trend, a past trend, a currenttrend, a target speed, a speed profile, a target speed profile, a pastspeed trend, the movement of the object, at least one characteristicsand/or spatial-temporal information of the object associated with themovement of object, a positional quantity of the object, an initialspeed of the object associated with the movement of the object, apredefined value, an initial width of the regression window, a timeduration, a value based on a carrier frequency of the wireless signal, abandwidth of the wireless signal, an amount of antennas associated withthe wireless multipath channel, a noise characteristics, and/or anadaptive value. The current time offset may be at the center, on theleft side, on the right side, and/or at another fixed relative location,of the regression window.

FIG. 1 illustrates an exemplary network environment 100 for eventdetection and monitoring in a venue, according to one embodiment of thepresent teaching. As shown in FIG. 1, the exemplary network environment100 includes a transmitter 110, an antenna 112, a wireless channel 130,an antenna 122, and a receiver 120. The antenna 112 is electricallycoupled to the transmitter 110; the antenna 122 is electrically coupledto the receiver 120.

In one embodiment, the transmitter 110 is located at a first position ina venue; while the receiver 120 is located at a second position in thevenue. The transmitter 110 is configured for transmitting a wirelesssignal through the wireless channel 130. The wireless channel 130 inthis example is a wireless multipath channel that is impacted by amotion of an object in the venue. According to various embodiments, theobject may be a human (e.g. a baby 142, or a patient 146) or a pet (e.g.a puppy 144). The receiver 120 in this example receives the wirelesssignal through the wireless multipath channel 130 and obtains at leastone time series of channel information (CI) of the wireless multipathchannel based on the wireless signal. Because the motion of the objectimpacts the wireless multipath channel through which the wireless signalis transmitted, the channel information 125 extracted from the wirelesssignal includes information related to the object motion.

In various embodiments, the transmitter 110 may be part of a Bot or aType 1 device placed in a venue, while the receiver 120 may be part ofan Origin or a Type 2 device placed in the venue. In variousembodiments, the Bot and/or the Origin may include (not shown) multipletransmitters, multiple receivers, and/or multiple transceivers. In oneembodiment, the antenna 112 and/or the antenna 122 is replaced with amulti-antenna array that can form a plurality of beams each of whichpoints in a distinct direction. The transmitter 110 can be configured towirelessly transmit signals having different types or functions.Similarly, the receiver 120 is configured to receive wireless signalshaving different types or functions. In one embodiment, the transmitter110 has at least one antenna; and the receiver 120 has at least oneantenna. Each of the at least one time series of CI is associated withone of the at least one antenna of the transmitter 110 and one of the atleast one antenna of the receiver 120.

FIG. 2 illustrates an exemplary diagram of a device 200 in a wirelessmonitoring system, according to one embodiment of the present teaching.The device 200 is an example of a device that can be configured toimplement the various methods described herein. According to variousembodiments, the device may be: a Type 1 device which is a Bot includinga transmitter, a Type 2 device which is an Origin including a receiver,an event recognition engine, and/or other components in FIGS. 1 and3-18. As shown in FIG. 2, the device 200 includes a housing 240containing a processor 202, a memory 204, a transceiver 210 comprising atransmitter 212 and a receiver 214, a synchronization controller 206, apower module 208, and an operation module 209.

In this embodiment, the processor 202 controls the general operation ofthe device 200 and can include one or more processing circuits ormodules such as a central processing unit (CPU) and/or any combinationof general-purpose microprocessors, microcontrollers, digital signalprocessors (DSPs), field programmable gate array (FPGAs), programmablelogic devices (PLDs), controllers, state machines, gated logic, discretehardware components, dedicated hardware finite state machines, or anyother suitable circuits, devices and/or structures that can performcalculations or other manipulations of data.

The memory 204, which can include both read-only memory (ROM) and randomaccess memory (RAM), can provide instructions and data to the processor202. A portion of the memory 204 can also include non-volatile randomaccess memory (NVRAM). The processor 202 typically performs logical andarithmetic operations based on program instructions stored within thememory 204. The instructions (a.k.a., software) stored in the memory 204can be executed by the processor 202 to perform the methods describedherein. The processor 202 and the memory 204 together form a processingsystem that stores and executes software. As used herein, “software”means any type of instructions, whether referred to as software,firmware, middleware, microcode, etc. which can configure a machine ordevice to perform one or more desired functions or processes.Instructions can include code (e.g., in source code format, binary codeformat, executable code format, or any other suitable format of code).The instructions, when executed by the one or more processors, cause theprocessing system to perform the various functions described herein.

The transceiver 210, which includes the transmitter 212 and receiver214, allows the device 200 to transmit and receive data to and from aremote device (e.g., an Origin or a Bot). An antenna 250 is typicallyattached to the housing 240 and electrically coupled to the transceiver210. In various embodiments, the device 200 includes (not shown)multiple transmitters, multiple receivers, and multiple transceivers. Inone embodiment, the antenna 250 is replaced with a multi-antenna array250 that can form a plurality of beams each of which points in adistinct direction. The transmitter 212 can be configured to wirelesslytransmit signals having different types or functions, such signals beinggenerated by the processor 202. Similarly, the receiver 214 isconfigured to receive wireless signals having different types orfunctions, and the processor 202 is configured to process signals of aplurality of different types.

In one embodiment, the device 200 may be a Bot or an Origin of awireless monitoring system. The wireless monitoring system may compriseat least one Bot and at least one Origin. The synchronization controller206 in this example may be configured to control the operations of thedevice 200 to be synchronized or un-synchronized with another device,e.g. another Origin or another Bot. In one embodiment, each of thedevice 200 and other Bots or Origins in the system may transmit orreceive the wireless signals individually and asynchronously.

The operation module 209 in this example may perform one or moreoperations for event detection and monitoring. The operation module 209may comprise one or more sub-modules to implement different methodsdisclosed herein. In one embodiment, the device 200 may be an eventrecognition engine of a wireless monitoring system, where the operationmodule 209 includes one or more of the components for recognizing and/orclassifying an event, e.g. door open, door close, door x % open, orother events related to security.

The power module 208 can include a power source such as one or morebatteries, and a power regulator, to provide regulated power to each ofthe above-described modules in FIG. 2. In some embodiments, if thedevice 200 is coupled to a dedicated external power source (e.g., a wallelectrical outlet), the power module 208 can include a transformer and apower regulator.

The various modules discussed above are coupled together by a bus system230. The bus system 230 can include a data bus and, for example, a powerbus, a control signal bus, and/or a status signal bus in addition to thedata bus. It is understood that the modules of the device 200 can beoperatively coupled to one another using any suitable techniques andmediums.

Although a number of separate modules or components are illustrated inFIG. 2, persons of ordinary skill in the art will understand that one ormore of the modules can be combined or commonly implemented. Forexample, the processor 202 can implement not only the functionalitydescribed above with respect to the processor 202, but also implementthe functionality described above with respect to the operation module209. Conversely, each of the modules illustrated in FIG. 2 can beimplemented using a plurality of separate components or elements.

In one embodiment, a method of a system having a processor, a memorycommunicatively coupled with the processor and a set of instructionsstored in the memory for recognizing events in a venue is disclosed. Thesystem comprises a first transmitter, a second transmitter, at least onefirst receiver, at least one second receiver, and an event recognitionengine. Each of the first transmitter, the second transmitter, the atleast one first receiver, the at least one second receiver, and theevent recognition engine can be implemented as the device 200. Themethod comprises: for each of at least one known event happening in avenue: transmitting, by an antenna of the first transmitter, arespective training wireless signal to the at least one first receiverthrough a wireless multipath channel in the venue in a training timeperiod associated with the known event, obtaining, asynchronously byeach of the at least one first receiver based on the training wirelesssignal, at least one time series of training channel information(training CI time series) of the wireless multipath channel between thefirst receiver and the first transmitter in the training time periodassociated with the known event, and pre-processing the at least onetraining CI time series; training, by the event recognition engine, atleast one classifier for the at least one known event based on the atleast one training CI time series; and for a current event happening inthe venue in a current time period, transmitting, by an antenna of thesecond transmitter, a current wireless signal to the at least one secondreceiver through the wireless multipath channel impacted by the currentevent in the venue in the current time period associated with thecurrent event, obtaining, asynchronously by each of the at least onesecond receiver based on the current wireless signal, at least one timeseries of current channel information (current CI time series) of thewireless multipath channel between the second receiver and the secondtransmitter in the current time period associated with the currentevent, pre-processing the at least one current CI time series, andapplying, by the event recognition engine, the at least one classifierto: classify at least one of: the at least one current CI time series, aportion of a particular current CI time series, and a combination of theportion of the particular current CI time series and a portion of anadditional CI time series, and associate the current event with at leastone of: a known event, an unknown event and another event. A training CItime series associated with a first receiver and a current CI timeseries associated with a second receiver have at least one of: differentstarting times, different time durations, different stopping times,different counts of items in their respective time series, differentsampling frequencies, different sampling periods between two consecutiveitems in their respective time series, and channel information (CI) withdifferent features.

The venue may be a house to be guarded. The known events may include:e.g. “front door open”, “back door open”, “all door close”, “bedroomwindow open”, etc. The set of devices (the first transmitter, the atleast one first receiver) used during training and the set of devices(the second transmitter, the at least one second receiver) used duringoperation may be the same or different in various situations. When theyare the same, once two devices (a transmitter and a receiver) areplugged in or installed, training can be performed and securityoperation can begin. In case the devices are moved, another round oftraining may be performed for security operation. Pre-processing mayinclude denoising and correction of phase error and other errors. Thesystem may perform monitoring and guard operation using a small sectionof CI time series (e.g. 0.1 second, or 1 second). In case a portion ofCI contains part of a training motion (e.g. the door moves from “close”to 30% open, and then does not move), the portion may be combined with apast recording of the door opening from 30% to fully open so that theclassification can be applied. If the CI associated with the currentevent does not match any of the training CI, the current event may bedeclared as an “unknown event”.

In one embodiment, each training CI time series and each current CI timeseries comprise at least two CI each with an associated time stamp. EachCI is associated with a respective time stamp. According to variousembodiments, the first transmitter and the second transmitter may be thesame device; the first transmitter and the second transmitter may be ata same location in the venue; the at least one first receiver and the atleast one second receiver may be the same; the at least one firstreceiver may be a permutation of the at least one second receiver; aparticular first receiver and a particular second receiver may be thesame device; at least one of: the at least one second receiver and asubset of the at least one second receiver, may be a subset of the atleast one first receiver; at least one of: the at least one secondreceiver and a subset of the at least one second receiver, may be apermutation of a subset of the at least one first receiver; at least oneof: the at least one first receiver and a subset of the at least onefirst receiver, may be a subset of the at least one second receiver; atleast one of: the at least one first receiver and a subset of the atleast one first receiver, may be a permutation of a subset of the atleast one second receiver; an antenna of the first transmitter and anantenna of the second transmitter may be at a same location in thevenue; at least one of: the at least one second receiver and a subset ofthe at least one second receiver, may be at a same respective locationas a subset of the at least one first receiver; at least one of: the atleast one first receiver and a subset of the at least one firstreceiver, may be at a same respective location as a subset of the atleast one second receiver; an antenna of the first transmitter and anantenna of the second transmitter may be at a same location in thevenue; at least one of: antennas of the at least one second receiver andantennas of a subset of the at least one second receiver, may be at samerespective locations as respective antennas of a subset of the at leastone first receiver; at least one of: antennas of the at least one firstreceiver and antennas of a subset of the at least one first receiver,may be at same respective locations as respective antennas of a subsetof the at least one second receiver.

In one embodiment, the pre-processing comprises at least one of: doingnothing, zero-padding, time-domain processing, frequency domainprocessing, time-frequency processing, spatially varying processing,temporally varying processing, adaptive processing, de-noising,smoothing, conditioning, enhancement, restoration, feature extraction,weighted averaging, averaging over antenna links, averaging overselected frequency, averaging over selected components, quantization,vector quantization, filtering, linear filtering, nonlinear filtering,low-pass filtering, bandpass filtering, high-pass filtering, medianfiltering, ranked filtering, quartile filtering, percentile filtering,mode filtering, linear filtering, nonlinear filtering, finite impulseresponse (FIR) filtering, infinite impulse response (IIR) filtering,moving average (MA) filtering, auto-regressive (AR) filtering,auto-regressive moving average (ARMA) filtering, thresholding, softthresholding, hard thresholding, soft clipping, local maximization,local minimization, optimization of a cost function, neural network,machine learning, supervised learning, unsupervised learning,semi-supervised learning, transformation, mapping, transform, inversetransform, integer transform, power-of-2 transform, real transform,floating-point transform, fixed-point transform, complex transform, fasttransform, Fourier transform, Laplace transform, Hadamard transform,Hilbert transform, sine transform, cosine transform, triangulartransform, wavelet transform, transformation, decomposition, selectivefiltering, adaptive filtering, derivative, first order derivative,second order derivative, higher order derivative, integration, zerocrossing, indicator function, absolute conversion, convolution,multiplication, division, another transform, another processing, anotherfilter, another function, and another preprocessing. In one embodiment,the pre-processing comprises at least one of: normalization, temporalnormalization, frequency normalization, magnitude correction, phasecorrection, phase cleaning, cleaning a phase associated with the channelinformation, normalizing components associated with the channelinformation, cleaning a phase of frequency components of the channelinformation, normalizing the frequency components, re-sampling,labeling, tagging, training, sorting, grouping, folding, thresholding,matched filtering, spectral analysis, clustering, quantization, vectorquantization, time correction, time base correction, time stampcorrection, sampling rate up-conversion/down-conversion, interpolation,intrapolation, extrapolation, subsampling, decimation, compression,expansion, encryption decryption, coding, storing, retrieving,transmitting, receiving, representing, merging, combining, splitting,tracking, monitoring, projection, orthogonal projection, non-orthogonalprojection, over-complete projection, decomposition,eigen-decomposition, principal component analysis (PCA), sparseapproximation, matching pursuit, and another operation etc.

In one embodiment, the method comprises aligning a first section of afirst time duration of a first CI time series (e.g. the training CI timeseries) and a second section of a second time duration of a second CItime series (e.g. the current CI time series), and computing a mapbetween items of the first section and items of the second section.

In one embodiment, the first CI time series may be processed by a firstoperation. The second CI time series may be processed by a secondoperation. At least one of: the first operation and the secondoperation, to comprise at least one of: subsampling, re-sampling,interpolation, filtering, transformation, feature extraction,pre-processing, and another operation.

In one embodiment, the method further comprises mapping a first item ofthe first section to a second item of the second section. In oneembodiment, the method further comprises mapping the first item of thefirst section also to another item of the second section. In oneembodiment, mapping another item of the first section to the second itemof the second section.

In one embodiment, at least one constraint is satisfied by at least onefunction of at least one of: the first item of the first section of thefirst CI time series, another item of the first CI time series, a timestamp of the first item, a time difference of the first item, a timedifferential of the first item, a neighboring time stamp of the firstitem, another time stamp associated with the first item, the second itemof the second section of the second CI time series, another item of thesecond CI time series, a time stamp of the second item, a timedifference of the second item, a time differential of the second item, aneighboring time stamp of the second item, and another time stampassociated with the second item. In one embodiment, one of the at leastone constraint is that a difference between the time stamp of the firstitem and the time stamp of the second item is upper-bounded by anadaptive upper threshold and lower-bounded by an adaptive lowerthreshold.

In one embodiment, the first section is the entire first CI time series,and/or the second section is the entire second CI time series. In oneembodiment, the first time duration is not equal to the second timeduration. In one embodiment, the method further comprises determining asection of a time duration of a CI time series adaptively.

In one embodiment, the method further comprises determining a startingtime and an ending time of a section. In one embodiment, the methodfurther comprises computing a tentative section of the CI time series,and determining the section by removing a beginning portion and anending portion of the tentative section.

In one embodiment, the method further comprises: determining a beginningportion of a tentative section by: considering items of the tentativesection with increasing time stamp as a current item iteratively, oneitem at a time; computing recursively an activity measure associatedwith at least one of: the current item associated with a current timestamp, past items of the tentative section with time stamps not largerthan the current time stamp, and future items of the tentative sectionwith time stamps not smaller than the current time stamp; adding thecurrent item to the beginning portion of the tentative section if acriterion associated with the activity measure is satisfied.

In one embodiment, the method further comprises: determining an endingportion of a tentative section by: considering items of the tentativesection with decreasing time stamp as a current item iteratively, oneitem at a time; iteratively computing and determining at least oneactivity measure associated with at least one of: the current itemassociated with a current time stamp, past items of the tentativesection with time stamps not larger than the current time stamp, andfuture items of the tentative section with time stamps not smaller thanthe current time stamp; and adding the current item to the endingportion of the tentative section if a criterion associated with the atleast one activity measure is satisfied. In one embodiment, thecriterion associated with the activity measure comprises at least oneof: the activity measure is smaller than an adaptive upper threshold,the activity measure is larger than an adaptive lower threshold, theactivity measure is smaller than an adaptive upper thresholdconsecutively for at least a predetermined amount of consecutive timestamps, the activity measure is larger than an adaptive lower thresholdconsecutively for at least another predetermined amount of consecutivetime stamps, the activity measure is smaller than an adaptive upperthreshold consecutively for at least a predetermined percentage of thepredetermined amount of consecutive time stamps, the activity measure islarger than an adaptive lower threshold consecutively for at leastanother predetermined percentage of the another predetermined amount ofconsecutive time stamps, another activity measure associated withanother time stamp associated with the current time stamp is smallerthan another adaptive upper threshold and larger than another adaptivelower threshold, at least one activity measure associated with at leastone respective time stamp associated with the current time stamp issmaller than respective upper threshold and larger than respective lowerthreshold, percentage of time stamps with associated activity measuresmaller than respective upper threshold and larger than respective lowerthreshold in a set of time stamps associated with the current time stampexceeds a threshold, and another criterion. In one embodiment, theactivity measure associated with an item at time T1 to comprise at leastone of: a first function of the item at time T1 and an item at timeT1−D1, wherein D1 is a pre-determined positive quantity, a secondfunction of the item at time T1 and an item at time T1+D1, a thirdfunction of the item at time T1 and an item at time T2, wherein T2 is apre-determined quantity, and a fourth function of the item at time T1and another item.

In one embodiment, at least one of: the first function, the secondfunction, the third function, and the fourth function, is at least oneof: a function F1(x, y, . . . ) with at least two scalar arguments: xand y, a function F2(X, Y, . . . ) with at least two vector arguments: Xand Y, and a function F3(X1, Y1, . . . ) with at least two arguments: X1and Y1. In one embodiment, the function F1 is a function of at least oneof the following: x, y, (x−y), (y−x), abs(x−y), x^a1, y^b1,abs(x^a1−y^b1), (x−y)^a1, (x/y), (x+a1)/(y+b1), (x^a1/y^b1), and((x/y)^a1−b1), wherein a1 and b1 are predetermined quantities; both Xand Y are n-tuples such that X=(x_1, x_2, . . . , x_n) and Y=(y_1, y_2,. . . , y_n); the function F2 is a function of at least one of thefollowing: x_i, y_i, (x_i−y_i), (y_i−x_i), abs(x_i−y_i), x_i^a2, y_i^b2,abs(x_i^a2−y_i^b2), (x_i−y_i)^a2, (x_i/y_i), (x_i+a2)/(y_i+b2),(x_i^a2/y_i^b2), and ((x_i/y_i)^a2−b2); i, ranging from 1 to n, is acomponent index of the n-tuples X and Y; both X1 and Y1 are n-tuplescomprising N components such that X1=(x1_1, x1_2, . . . , x1_N) andY1=(y1_1, y1_2, . . . , y1_N); the function F3 comprises acomponent-by-component summation of another function of at least one ofthe following: x1_j, y1_j, (x1_j−y1_j), (y1_j−x1_j), abs(x1_j−y1_j),x1_j^a3, y1_j^b3, abs(x1_j^a3−y1_j^b3), (x1_j−y1_)^a3, (x1_j/y1_j),(x1_j+a3)/(y1_j+b3), (x1_j^a3/y1_j^b3), and ((x1_j/y1_j)^a3−b3); and j,ranging from 1 to N, is a component index of the n-tuples X1 and Y1.

In one embodiment, the method further comprises computing the map usingdynamic time warping (DTW). In one embodiment, the dynamic time warping(DTW) comprises a constraint on at least one of: the map, the items ofthe first CI time series, the items of the second CI time series, thefirst time duration, the second time duration, the first section, andthe second section.

In one embodiment, the method further comprises: aligning a firstsection of a first time duration of a first CI time series and a secondsection of a second time duration of a second CI time series, computinga map comprising more than one links between first items of the firstsection and second items of the second section, each link associating afirst item with a first time stamp with a second item with a second timestamp, computing a mismatch cost between the aligned first section andthe aligned second section, and applying the at least one classifierbased on the mismatch cost. In one embodiment, the mismatch costcomprises a function of: an item-wise cost between a first item of thefirst section of the first CI time series and a second item of thesecond section of the second CI time series associated with the firstitem by a link of the map, and a link-wise cost associated with the linkof the map. In one embodiment, the aligned first section and the alignedsecond section are represented respectively as a first vector and asecond vector, both of same vector length; the mismatch cost to compriseat least one of: an inner product, an inner-product-like quantity, aquantity based on correlation, a quantity based on covariance, adiscriminating score, a distance, a Euclidean distance, an absolutedistance, an L_1 distance, an L_2 distance, an L_k distance, a weighteddistance, a distance-like quantity and another similarity value, betweenthe first vector and the second vector; and the mismatch cost isnormalized by the vector length. In one embodiment, a parameter derivedfrom the mismatch cost between the first section of the first timeduration of the first CI time series and the second section of thesecond time duration of the second CI time series is modeled with astatistical distribution; and at least one of: a scale parameter, alocation parameter and another parameter, of the statisticaldistribution is estimated. In one embodiment, the first section of thefirst time duration of the first CI time series is a sliding section ofthe first CI time series, and the second section of the second timeduration of the second CI time series is a sliding section of the secondCI time series.

In one embodiment, the method further comprises: applying a firstsliding window to the first CI time series and a corresponding secondsliding window to the second CI time series, aligning the first slidingwindow of the first CI time series and the corresponding second slidingwindow of the second CI time series, computing mismatch cost between thealigned first sliding window of the first CI time series and thecorresponding aligned second sliding window of the second CI timeseries, and associating the current event with at least one of: theknown event, the unknown event and the another event, based on themismatch cost. In one embodiment, the method further comprises: applyingthe classifier to at least one of: the first section of the first timeduration of the first CI time series, and the second section of thesecond time duration of the second CI time series. In one embodiment,the method further comprises: applying the at least one classifier tomore than one first sections of the first CI time series and more thanone respective second sections of the second CI time series, obtainingat least one tentative classification results, each tentativeclassification result being associated with a respective first sectionand a respective second section, and associating the current event withat least one of: the known event, the unknown event and the anotherevent, based on a largest number of the at least one tentativeclassification results associated with at least one of: the known event,the unknown event and the another event. In one embodiment, the methodfurther comprises: associating the current event with at least one of:the known event, the unknown event and the another event, based on themismatch cost. In one embodiment, the method further comprises:associating the current event with at least one of: the known event, theunknown event and the another event, based on the mismatch cost andadditional mismatch cost associated with at least one additional sectionof the first CI time series and at least one additional section of thesecond CI time series.

In one embodiment, the known events comprise at least one of: a doorclosed event, a door open event, a window closed event, a window openevent, a multi-state event, an on-state event, an off-state event, anintermediate state event, a continuous state event, a discrete stateevent, a continuous event, a discrete event, a sequential event, aproximity event, a presence event, an absence event, an entrance event,an exit event, a movement event, an approaching event, a receding event,a progressing event, an action event, a factory event, a logistic event,a navigation event, an alignment event, a vehicle event, a parkingevent, a machine event, a manufacturing event, a robot event, a droneevent, a landing event, a take-off event, a launching event, an accidentevent, a collision event, an impact event, a striking event, a breakingevent, a breakthrough event, an explosive event, an office event, akeyboard event, a computer interface event, a home event, a cleaningevent, a hygiene event, a pet event, an insect event, a kitchen event, acooking event, a meal event, a laundry event, a furniture movementevent, a bathroom event, a bedroom event, a living room event, a familyroom event, a dining room event, a garage event, a foyer event, astaircase event, a basement event, an attic event, a pantry event, afire event, a water event, a shower event, an air-flow event, a fanevent, a heat-related event, a light-related event, a wind event, asit-down event, a stand-up event, a lie-down event, a get-up event, arolling event, a turning event, a repeated event, an exercise event, arelaxation event, a resting event, a sleeping event, a fall-down event,a staircase event, a party event, a running event, a walking event, areading event, a musical event, a sound event, an instrument event, ahuman motion event, a baby event, a child event, an older adult event, agesture event, a handwriting event, a drawing event, a home event, anoffice event, a pet event, a sleeping event, a human-present event, ahuman-absent event, a sign-of-life-present event, and asign-of-life-absent event.

In one embodiment, instead of using the whole CSI (which has manycomponents), the system can train (e.g. by using some training algorithmon some training data) a data reduction scheme (a projection). Forexample, one may find a projection to reduce a 256-dimension CSI to a10-dimension feature space. One training method is principal componentanalysis (PCA). In one embodiment, the method further comprises:training a projection for each CI using a dimension reduction methodbased on the training CI time series associated with the at least oneknown event, wherein the dimension reduction method to comprise at leastone of: principal component analysis (PCA), PCA with different kernel,independent component analysis (ICA), Fisher linear discriminant, vectorquantization, supervised learning, unsupervised learning,self-organizing maps, auto-encoder, neural network, deep neural network,and another method, applying the projection to all CI, training the atleast one classifier of the at least one event based on the projectionand the training CI time series associated with the at least one event,and classifying the at least one current CI time series based on theprojection and the at least one current CI time series.

Sometimes the environment is changed over time (e.g. some furniture suchas sofa or table is moved) such that the previously trained classifieris no longer appropriate or valid. Thus retraining is performed. Inother words, the classifier is re-trained, adapted, updated, orrefreshed. In one embodiment, the method further comprises: re-trainingthe projection using at least one of: the dimension reduction method,and another dimension reduction method, based on at least one of: theprojection before the re-training, the training CI time series, at leastone current CI time series obtained as of the re-training of theprojection, and additional training CI time series, wherein the anotherdimension reduction method to comprise at least one of: a simplifieddimension reduction method, principal component analysis (PCA), PCA withdifferent kernels, independent component analysis (ICA), Fisher lineardiscriminant, vector quantization, supervised learning, unsupervisedlearning, self-organizing maps, auto-encoder, neural network, deepneural network, and yet another method; re-training the at least oneclassifier of the at least one event based on at least one of: there-trained projection, the training CI time series associated with theat least one events, and at least one current CI time series; andclassifying the at least one current CI time series based on there-trained projection, the re-trained classifier, and the current CItime series.

The CSI may be preprocessed before projection. For example, one mayretain only the magnitude of each CSI component as the phase tends to besensitive to noise. If the CSI has 128 components, the preprocessingwould give 128 new components each being magnitude of correspondcomponent of the CSI. In one embodiment, each CI to comprise a vector ofcomplex values; each complex value is preprocessed to give the magnitudeof the complex value; each CI is preprocessed to give a vector ofnon-negative real numbers comprising the magnitude of correspondingcomplex values.

During training, some training CI time series may be weighted more. Forexample, an important training CI time series may be used more than oncein the training, or may have a larger weight in the training cost. Inone embodiment, each training CI time series is weighted in the trainingof the projection. The projection may have N (more than 1) components,e.g. N=5, or 10, or 15, or 20, etc. In one embodiment, the projectioncomprises more than one projected components. The projection may have atleast one most significant component. For example, we may use PCA andretain the N components with highest energy. In one embodiment, theprojection comprises at least one projected component that is beneficialfor the at least one classifier.

In one embodiment, the current section may be 2 seconds long, with 2Mcurrent CSI each with corresponding time stamp. A trained representativesection of “front door open” may be 3 seconds long, with 3M training CSI(i.e. duration is different). In DTW, one can establish correspondencebetween time stamps of the 2M current CSI and time stamps of the 3Mtraining CSI. Then one can compute a mismatch cost between the twosections. Each mismatch cost is a “distance” between a current CSI and atraining CSI. To compute the distance, one can compute a CSI-distancebetween a current CSI and a training CSI, and one can compute theCSI-distance 3M times. The distance may be normalized by the amount ofaligned CSI (3M, which is the largest of the two section length) so thatdistance associated with different events can be compared. In oneembodiment, the method further comprises: for at least one first sectionof a first time duration of the current CI time series: for each of theat least one known event: determining a respective second section of arespective second time duration of a respective representative trainingCI time series of the respective event, aligning the first section andthe respective second section, and computing a mismatch cost between thealigned first section and the aligned respective second section;applying the at least one classifier; and obtaining a tentativeclassification result based on the mismatch costs; and associating theat least one section with at least one of: one of the at least one knownevent, an unknown event and the another event based on the at least onetentative classification result.

In one embodiment, a particular classifier associates a first section ofthe first time duration of the current CI time series to a known eventwhose respective second section has smallest mismatch cost with thefirst section. The classifier may choose the event that gives thatsmallest (normalized) mismatch cost. In one embodiment, the methodfurther comprises: computing how many times each known event achievesthe smallest mismatch cost; and associating the at least one sectionwith at least one of: a known event that achieves the smallest mismatchcost for the most times, a known event that achieves smallest overallmismatch cost, wherein overall mismatch cost is a weighted average of atleast one mismatch cost associated with the at least one first sections,a known event that achieves smallest of another overall cost, and anunknown event. The associated event is the unknown event in at least oneof the following situations: none of the events achieve mismatch costlower than a first threshold T1 in a sufficient percentage of the atleast one first section; and none of the events achieve an overallmismatch cost lower than a second threshold T2.

In one embodiment, the trained representative CI time series is trainedbased on a large number of training CI time series of the eventcollected during training phase/stage (e.g. the event may befront-door-open). Distance (mismatch cost) between CI time series may bedefined as follows: DTW is applied to a pair (any pair) of training CItime series (or an “activity” or “significant” section of it) so thatnormalized mismatch cost can be computed. The trained representative maybe obtained by clustering, discriminative training, or some kind ofmachine learning. Important training CI time series may be weighted morethan others. In one embodiment, the trained representative CI timeseries associated with the known event is obtained based on the at leastone training CI time series associated with the known event. In oneembodiment, the trained representative CI time series associated withthe known event is one of the at least one training CI time seriesassociated with the known event.

In one embodiment, the aggregate mismatch associated with a trainingdata point (a training CI time series) among a set of training datapoints (e.g. a set of training CI time series for “front door open”) isa measure of how good it is to represent a set of training data points.The aggregate mismatch can be an average (or weighted average, or otherfunction of) distance between the training data point and each of theother training data point in the set. A good representative data pointis close to the other data points. Training representative CI timeseries is one of the training CI time series. In one embodiment, thetrained representative CI time series associated with the known event isa particular one of the at least one training CI time series associatedwith the known event such that it has smallest aggregate mismatch amongthe at least one training CI time series; the aggregate mismatch of aparticular training CI time series is a function of at least onemismatch cost between the particular training CI time series and each ofthe rest of the at least one training CI time series aligned with theparticular training CI time series; and the function to comprise atleast one of: average, weighed average, mean, trimmed mean, median,mode, arithmetic mean, geometric mean, harmonic mean, truncated mean,generalized mean, power mean, f-mean, interquartile mean, and anothermean.

In one embodiment, training representative CI time series is not one ofthe training CI time series. It is computed by minimizing aggregatemismatch. Its duration (length) needs to be determined. In oneembodiment, a particular trained representative CI time seriesassociated with a particular known event has a particular time duration;the particular trained representative CI time series of the particulartime duration is aligned with each of the at least one training CI timeseries associated with the known event and respective mismatch cost iscomputed; the particular trained representative CI time series tominimize an aggregate mismatch with respect to the at least one trainingCI time series; the aggregate mismatch of a CI time series is a functionof at least one mismatch cost between the CI time series and each of atleast one training CI time series aligned with the CI time series; andthe function to comprise at least one of: average, weighed average,mean, trimmed mean, median, mode, arithmetic mean, geometric mean,harmonic mean, truncated mean, generalized mean, power mean, f-mean,interquartile mean, and another mean.

In one embodiment, even the duration/length of the representative CItime series (section) is obtained by training. Different duration can bechecked (trial computed). Different duration of the section can beexamined. In one embodiment, the particular time duration minimizes theaggregate mismatch among more than one candidate time durations of theparticular trained representative CI time series. In one embodiment, foreach of at least one candidate time duration, the optimal trainedrepresentative CI time series with the candidate time duration iscomputed; the particular time duration is chosen as the candidate timeduration that gives minimal normalized aggregate mismatch; and thenormalized aggregated mismatch associated with a candidate time durationis the respective aggregate mismatch normalized by the candidate timeduration.

In another embodiment, the particular time duration is obtained byperforming a search among the at least one candidate time durations withthe cost function being at least one of: normalized aggregate mismatch,hybrid normalized aggregate mismatch, combination normalized aggregatematch, a simplified mismatch, and another cost function; the normalizedaggregated mismatch associated with a candidate time duration is therespective aggregate mismatch normalized by the candidate time duration;and the search to comprise at least one of: brute force exhaustivesearch, gradient descent, steepest descent, stochastic search, geneticsearch, predictive search, local search, multi-resolution search,hierarchical search, constrained search, unconstrained search, fastsearch, simplified search, and another search. In one embodiment, theparticular time duration and the particular trained representative CItime series with the particular time duration are computed iteratively;a current time duration is initialized as one of the candidate timedurations; for the current time duration, a current optimal trainedrepresentative CI time series with the current time duration iscomputed; and for the current optimal trained representative CI timeseries with the current time duration, the current time duration ischanged to give smaller normalized aggregate mismatch.

The initial duration/length of current section of the current CI timeseries (section) may be equal to a “typical” duration of the event (e.g.“front-door-open”). In one embodiment, the current time duration isinitialized with a value based on durations associated with the at leastone training CI time series associated with the known event. The initialduration/length of current section of the current CI time series(section) may be computed based on duration of training CI time seriesof the event (e.g. “front-door-open”). In one embodiment, the currenttime duration is initialized with a value based on durations associatedwith the at least one training CI time series associated with the knownevent.

In one embodiment, each pair of transmitting antenna and receivingantenna corresponds to a current CI time series. For example, eachcurrent CI time series is associated with an antenna of the secondtransmitter and an antenna of a second Type 2 heterogeneous wirelessdevice.

FIG. 3 illustrates an exemplary flow of detecting indoor events usingtime-reversal technology, according to one embodiment of the presentteaching. In one embodiment, the present teaching discloses methods,apparatus, devices, and software of a wireless monitoring systemcomprising training at least one classifier of at least one known eventsin a venue based on training CI time series associated with the at leastone events. For each of the at least one known event happening in thevenue in a respective training time period associated with the knownevent, a respective series of training probe signals is transmitted byan antenna of a first wireless device using a processor, a memory and aset of instructions of the first wireless device to at least oneheterogeneous first target wireless receiver through a wirelessmultipath channel in the venue in the respective training time period.At least one respective time series of training channel information(training CI time series) is obtained asynchronously by each of the atleast one heterogeneous first target wireless receiver from the(respective) series of training probe signals. The channel information(CI) is CI of the wireless multipath channel between the heterogeneousfirst target wireless receiver and the first wireless device in thetraining time period associated with the known event. The at least onetraining CI time series is preprocessed. For a current event happeningin the venue in a current time period, a series of current probe signalsis transmitted by an antenna of a second wireless device using aprocessor, a memory and a set of instructions of the second wirelessdevice to at least one heterogeneous second target wireless receiverthrough the wireless multipath channel impacted by the current event inthe venue in the current time period associated with the current event.At least one time series of current channel information (current CI timeseries) is obtained asynchronously by each of the at least oneheterogeneous second target wireless receiver from the series of currentprobe signals. The channel information (CI) is CI of the wirelessmultipath channel between the heterogeneous second target wirelessreceiver and the second wireless device in the current time periodassociated with the current event. The at least one current CI timeseries is preprocessed.

The at least one classifier is applied to classify at least one currentCI time series obtained from the series of current probe signals by theat least one heterogeneous second target wireless receiver, to classifyat least one portion of a particular current CI time series, and/or toclassify a combination of the at least one portion of the particularcurrent CI time series and another portion of another CI time series.The at least one classifier is also applied to associate the currentevent with a known event, an unknown event and/or another event. Eachseries of probe signals may comprise at least two CI each with anassociated time stamp. Each CI may be associated with a respective timestamp. A current CI time series associated with a heterogeneous secondtarget wireless receiver and another current CI time series associatedwith another heterogeneous second target wireless receiver may havedifferent starting time, different time duration, different stoppingtime, different count of items in the time series, different samplingfrequency, different sampling period between two consecutive items inthe time series, and/or channel information (CI) with differentfeatures.

The first wireless device and the second wireless device may be the samedevice. The first wireless device and the second wireless device may beat same location in the venue. The at least one heterogeneous firsttarget wireless receiver and the at least one heterogeneous secondtarget wireless receiver may be the same. The at least one heterogeneousfirst target wireless receiver may be a permutation of the at least oneheterogeneous second target wireless receiver. A particularheterogeneous first target wireless receiver and a particularheterogeneous second target wireless receiver may be the same device.The at least one heterogeneous first target wireless receiver and/or asubset of the at least one heterogeneous first target wireless receivermay be a subset of the at least one heterogeneous second target wirelessreceiver. The at least one heterogeneous second target wireless receiverand/or a subset of the at least one heterogeneous second target wirelessreceiver may be a subset of the at least one heterogeneous first targetwireless receiver.

The at least one heterogeneous first target wireless receiver and/or asubset of the at least one heterogeneous first target wireless receivermay be a permutation of a subset of the at least one heterogeneoussecond target wireless receiver. The at least one heterogeneous secondtarget wireless receiver and/or a subset of the at least oneheterogeneous second target wireless receiver may be a permutation of asubset of the at least one heterogeneous first target wireless receiver.The at least one heterogeneous second target wireless receiver and/or asubset of the at least one heterogeneous second target wireless receivermay be at same respective location as a subset of the at least oneheterogeneous first target wireless receiver. The at least oneheterogeneous first target wireless receiver and/or a subset of the atleast one heterogeneous first target wireless receiver may be at samerespective location as a subset of the at least one heterogeneous secondtarget wireless receiver.

The antenna of the first wireless device and the antenna of the secondwireless device may be at same location in the venue. Antenna(s) of theat least one heterogeneous second target wireless receiver and/orantenna(s) of a subset of the at least one heterogeneous second targetwireless receiver may be at same respective location as respectiveantenna(s) of a subset of the at least one heterogeneous first targetwireless receiver. Antenna(s) of the at least one heterogeneous firsttarget wireless receiver and/or antenna(s) of a subset of the at leastone heterogeneous first target wireless receiver may be at samerespective location(s) as respective antenna(s) of a subset of the atleast one heterogeneous second target wireless receiver.

The pre-processing may comprise at least one of: doing nothing,zero-padding, time-domain processing, frequency domain processing,time-frequency processing, spatially varying processing, temporallyvarying processing, adaptive processing, de-noising, smoothing,conditioning, enhancement, restoration, feature extraction, weightedaveraging, averaging over antenna links, averaging over selectedfrequency, averaging over selected components, quantization, vectorquantization, filtering, linear filtering, nonlinear filtering, low-passfiltering, bandpass filtering, high-pass filtering, median filtering,ranked filtering, quartile filtering, percentile filtering, modefiltering, linear filtering, nonlinear filtering, finite impulseresponse (FIR) filtering, infinite impulse response (IIR) filtering,moving average (MA) filtering, auto-regressive (AR) filtering,auto-regressive moving average (ARMA) filtering, thresholding, softthresholding, hard thresholding, soft clipping, local maximization,local minimization, optimization of a cost function, neural network,machine learning, supervised learning, unsupervised learning,semi-supervised learning, transformation, mapping, transform, inversetransform, integer transform, power-of-2 transform, real transform,floating-point transform, fixed-point transform, complex transform, fasttransform, Fourier transform, Laplace transform, Hadamard transform,Hilbert transform, sine transform, cosine transform, triangulartransform, wavelet transform, transformation, decomposition, selectivefiltering, adaptive filtering, derivative, first order derivative,second order derivative, higher order derivative, integration, zerocrossing, indicator function, absolute conversion, convolution,multiplication, division, another transform, another processing, anotherfilter, another function, and/or another preprocessing. Thepre-processing may also comprise at least one of: normalization,temporal normalization, frequency normalization, magnitude correction,phase correction, phase cleaning, cleaning a phase associated with thechannel information, normalizing components associated with the channelinformation, cleaning a phase of frequency components of the channelinformation, normalizing the frequency components, re-sampling,labeling, tagging, training, sorting, grouping, folding, thresholding,matched filtering, spectral analysis, clustering, quantization, vectorquantization, time correction, time base correction, time stampcorrection, sampling rate up-conversion/down-conversion, interpolation,intrapolation, extrapolation, subsampling, decimation, compression,expansion, encryption decryption, coding, storing, retrieving,transmitting, receiving, representing, merging, combining, splitting,tracking, monitoring, projection, orthogonal projection, non-orthogonalprojection, over-complete projection, decomposition,eigen-decomposition, principal component analysis (PCA), sparseapproximation, matching pursuit, and/or another operation, etc.

A first section of a first time duration of the first CI time series anda second section of a second time duration of the second section of thesecond CI time series may be aligned. A map between items of the firstsection and items of the second section may be computed. The first CItime series may be processed by a first operation. The second CI timeseries may be processed by a second operation. The first operationand/or the second operation may comprise at least one of: subsampling,re-sampling, interpolation, filtering, transformation, featureextraction, pre-processing, and/or another operation.

A first item of the first section may be mapped to a second item of thesecond section. The first item of the first section may also be mappedto another item of the second section. Another item of the first sectionmay also be mapped to the second item of the second section. At leastone function of at least one of: the first item of the first section ofthe first CI time series, another item of the first CI time series, atime stamp of the first item, a time difference of the first item, atime differential of the first item, a neighboring time stamp of thefirst item, another time stamp associated with the first item, thesecond item of the second section of the second CI time series, anotheritem of the second CI time series, a time stamp of the second item, atime difference of the second item, a time differential of the seconditem, a neighboring time stamp of the second item, and another timestamp associated with the second item, may satisfy at least oneconstraint. One constraint may be that a difference between the timestamp of the first item and the time stamp of the second item may beupper-bounded by an adaptive upper threshold and lower-bounded by anadaptive lower threshold. The first section may be the entire first CItime series. The second section may be the entire second CI time series.The first time duration may be equal to the second time duration.

A section of a time duration of a CI time series may be determinedadaptively. A tentative section of the CI time series may be computed. Astarting time and an ending time of a section (e.g. the tentativesection, the section) may be determined. The section may be determinedby removing a beginning portion and an ending portion of the tentativesection. A beginning portion of a tentative section may be determined asfollows. Iteratively, items of the tentative section with increasingtime stamp may be considered as a current item, one item at a time. Ineach iteration, at least one activity measure may be computed and/orconsidered. The at least one activity measure may be associated with atleast one of: the current item associated with a current time stamp,past items of the tentative section with time stamps not larger than thecurrent time stamp, and/or future items of the tentative section withtime stamps not smaller than the current time stamp. The current itemmay be added to the beginning portion of the tentative section if atleast one criterion associated with the at least one activity measure issatisfied. The at least one criterion associated with the activitymeasure may comprise at least one of: (a) the activity measure issmaller than an adaptive upper threshold, (b) the activity measure islarger than an adaptive lower threshold, (c) the activity measure issmaller than an adaptive upper threshold consecutively for at least apredetermined amount of consecutive time stamps, (d) the activitymeasure is larger than an adaptive lower threshold consecutively for atleast another predetermined amount of consecutive time stamps, (e) theactivity measure is smaller than an adaptive upper thresholdconsecutively for at least a predetermined percentage of thepredetermined amount of consecutive time stamps, (f) the activitymeasure is larger than an adaptive lower threshold consecutively for atleast another predetermined percentage of the another predeterminedamount of consecutive time stamps, (g) another activity measureassociated with another time stamp associated with the current timestamp is smaller than another adaptive upper threshold and larger thananother adaptive lower threshold, (h) at least one activity measureassociated with at least one respective time stamp associated with thecurrent time stamp is smaller than respective upper threshold and largerthan respective lower threshold, (i) percentage of time stamps withassociated activity measure smaller than respective upper threshold andlarger than respective lower threshold in a set of time stampsassociated with the current time stamp exceeds a threshold, and (j)another criterion.

An activity measure associated with an item at time T1 may comprise atleast one of: (1) a first function of the item at time T1 and an item attime T1−D1, wherein D1 is a pre-determined positive quantity (e.g. aconstant time offset), (2) a second function of the item at time T1 andan item at time T1+D1, (3) a third function of the item at time T1 andan item at time T2, wherein T2 is a pre-determined quantity (e.g. afixed initial reference time; T2 may be changed over time; T2 may beupdated periodically; T2 may be the beginning of a time period and T1may be a sliding time in the time period), and (4) a fourth function ofthe item at time T1 and another item.

At least one of: the first function, the second function, the thirdfunction, and/or the fourth function may be a function (e.g. F(X, Y, . .. )) with at least two arguments: X and Y. The function (e.g. F) may bea function of at least one of: X, Y, (X−Y), (Y−X), abs(X−Y), X^a, Y^b,abs(X^a−Y^b), (X−Y)^a, (X/Y), (X+a)/(Y+b), (X^a/Y^b), and ((X/Y)^a−b),wherein a and b may be some predetermined quantities. For example, thefunction may simply be abs(X−Y), or (X−Y)^2, (X−Y)^4. The function maybe a robust function. For example, the function may be (X−Y)^2 when abs(X−Y) is less than a threshold T, and (X−Y)+a when abs(X−Y) is largerthan T. Alternatively, the function may be a constant when abs(X−Y) islarger than T. The function may also be bounded by a slowly increasingfunction when abs(X−y) is larger than T, so that outliers cannotseverely affect the result. Another example of the function may be(abs(X/Y)−a), where a=1. In this way, if X=Y (i.e. no change or noactivity), the function will give a value of 0. If X is larger than Y,(X/Y) will be larger than 1 (assuming X and Y are positive) and thefunction will be positive. And if X is less than Y, (X/Y) will besmaller than 1 and the function will be negative. In another example,both X and Y may be n-tuples such that X=(x_1, x_2, . . . , x_n) andY=(y_1, y_2, . . . , y_n). The function may be a function of at leastone of: x_i, y_i, (x_i−y_i), (y_i−x_i), abs(x_i−y_i), x_i^a, y_i^b,abs(x_i^a−y_i^b), (x_i−y_i)^a, (x_i/y_i), (x_i+a)/(y_i+b),(x_i^a/y_i^b), and ((x_i/y_i)^a−b), wherein i is a component index ofthe n-tuple X and Y, and 1<=i<=n. E.g. component index of x_1 is i=1,component index of x_2 is i=2. The function may comprise acomponent-by-component summation of another function of at least one ofthe following: x_i, y_i (x_i−y_i), (y_i−x_i), abs(x_i−y_i), x_i^a,y_i^b, abs(x_i^a−y_i^b), (x_i−y_i)^a, (x_i/y_i), (x_i+a)/(y_i+b),(x_i^a/y_i^b), and ((x_i/y_i)^a−b), wherein i is the component index ofthe n-tuple X and Y. For example, the function may be in a form ofsum_{i=1}^n (abs(x_i/y_i)−1)/n, or sum_{i=1}^n w_i*(abs(x^i/y^i)−1),where w_i is some weight for component i.

The map may be computed using dynamic time warping (DTW). The DTW maycomprise a constraint on at least one of: the map, the items of thefirst CI time series, the items of the second CI time series, the firsttime duration, the second time duration, the first section, and/or thesecond section. Suppose in the map, the i^{th} domain item is mapped tothe j^{th} range item. The constraint may be on admissible combinationof i and j (constraint on relationship between i and j). Pairwisemismatch between a first section of a first time duration of a first CItime series and a second section of a second time duration of a secondCI time series may be computed. The first section and the second sectionmay be aligned such that a map comprising more than one links may beestablished between first items of the first CI time series and seconditems of the second CI time series. With each link, one of the firstitems with a first time stamp may be associated with one of the seconditems with a second time stamp.

A mismatch cost between the aligned first section and the aligned secondsection may be computed. The mismatch cost may comprise a function of:an item-wise mismatch cost between a first item and a second itemassociated by a particular link of the map, and a link-wise costassociated with the particular link of the map. The aligned firstsection and the aligned second section may be represented respectivelyas a first vector and a second vector of same vector length. Themismatch cost may comprise at least one of: an inner product, aninner-product-like quantity, a quantity based on correlation, a quantitybased on covariance, a discriminating score, a distance, a Euclideandistance, an absolute distance, an Lk distance (e.g. L1, L2, . . . ), aweighted distance, a distance-like quantity and/or another similarityvalue, between the first vector and the second vector. The mismatch costmay be normalized by the respective vector length. A parameter derivedfrom the pairwise mismatch between the first section of the first timeduration of the first CI time series and the second section of thesecond time duration of the second CI time series may be modeled with astatistical distribution. At least one of: a scale parameter, a locationparameter and/or another parameter, of the statistical distribution maybe estimated.

The first section of the first time duration of the first CI time seriesmay be a sliding section of the first CI time series. The second sectionof the second time duration of the second CI time series may be asliding section of the second CI time series. A first sliding window maybe applied to the first CI time series and a corresponding secondsliding window may be applied to the second CI time series. The firstsliding window of the first CI time series and the corresponding secondsliding window of the second CI time series may be aligned.

Pairwise mismatch between the aligned first sliding window of the firstCI time series and the corresponding aligned second sliding window ofthe second CI time series may be computed. The current event may beassociated with at least one of: the known event, the unknown eventand/or the another event, based on the pairwise mismatch. The classifiermay be applied to at least one of: the first section of the first timeduration of the first CI time series, and/or the second section of thesecond time duration of the second CI time series.

The current event may be associated with at least one of: the knownevent, the unknown event and/or the another event, based on a largestnumber of tentative classification results in more than one sections ofthe first CI time series and corresponding more than sections of thesecond CI time series. For example, the current event may be associatedwith a particular known event if the pairwise mismatch points to theparticular known event for N consecutive times (e.g. N=10). In anotherexample, the current event may be associated with a particular knownevent if the percentage of pairwise mismatch within the immediate past Nconsecutive N pointing to the particular known event exceeds a certainthreshold (e.g. >80%).

The current event may be associated with at least one of: the knownevent, the unknown event and/or the another event, based on the mismatchcost. The current event may be associated with at least one of: theknown event, the unknown event and/or the another event, based on themismatch cost and additional mismatch cost associated with at least oneadditional section of the first CI time series and at least oneadditional section of the second CI time series.

The known events may comprise at least one of: a door closed event, adoor open event, a window closed event, a window open event, amulti-state event, an on-state event, an off-state event, anintermediate state event, a continuous state event, a discrete stateevent, a human-present event, a human-absent event, asign-of-life-present event, and/or a sign-of-life-absent event.

A projection for each channel information may be trained using adimension reduction method based on the training CI time series. Thedimension reduction method may comprise at least one of: principalcomponent analysis (PCA), PCA with different kernel, independentcomponent analysis (ICA), Fisher linear discriminant, vectorquantization, supervised learning, unsupervised learning,self-organizing maps, auto-encoder, neural network, deep neural network,and/or another method. The projection may be applied to at least one of:the training CI time series associated with the at least one event,and/or the current CI time series, for the at least one classifier.

The at least one classifier of the at least one event may be trainedbased on the projection and the training CI time series associated withthe at least one event. The at least one current CI time series may beclassified based on the projection and the current CI time series. Theprojection may be re-trained using at least one of: the dimensionreduction method, and another dimension reduction method, based on atleast one of: the projection before the re-training, the training CItime series, at least one current CI time series before retraining theprojection, and/or additional training CI time series.

The another dimension reduction method may comprise at least one of: asimplified dimension reduction method, principal component analysis(PCA), PCA with different kernels, independent component analysis (ICA),Fisher linear discriminant, vector quantization, supervised learning,unsupervised learning, self-organizing maps, auto-encoder, neuralnetwork, deep neural network, and/or yet another method.

The at least one classifier of the at least one event may be re-trainedbased on at least one of: the re-trained projection, the training CItime series associated with the at least one events, and/or at least onecurrent CI time series. The at least one current CI time series may beclassified based on: the re-trained projection, the re-trainedclassifier, and/or the current CI time series. Each CI may comprise avector of complex values. Each complex value may be preprocessed to givethe magnitude of the complex value. Each CI may be preprocessed to givea vector of non-negative real numbers comprising the magnitude ofcorresponding complex values. Each training CI time series may beweighted in the training of the projection. The projection may comprisemore than one projected components. The projection may comprise at leastone most significant projected component. The projection may comprise atleast one projected component that may be beneficial for the at leastone classifier.

A particular classifier may be configured to compute, for a firstsection of a first time duration of the current CI time series and foreach of the at least one known event, pairwise mismatch between thefirst section of the first time duration of the current CI time seriesand a second section of a second time duration of a trainedrepresentative CI time series associated with the known event. Theparticular classifier may associate the first section of the first timeduration of the current CI time series to a known event whose secondsection has smallest pairwise mismatch with the first section. Theparticular classifier may associate the first section of the first timeduration of the current CI time series to a known event whose secondsection has smallest pairwise mismatch with the first section, if aprevious first section of another first time duration of the current CItime series has smallest pairwise mismatch with another second sectionof another second time duration of the trained CI time series associatedwith the known event.

The trained representative CI time series associated with the knownevent may be obtained based on the at least one training CI time seriesassociated with the known event. The trained representative CI timeseries associated with the known event may be one of the at least onetraining CI time series associated with the known event. The trainedrepresentative CI time series associated with the known event may be aparticular one of the at least one training CI time series associatedwith the known event such that it has smallest aggregate mismatchcompared with the at least one training CI time series.

The aggregate mismatch may be a function of at least one pairwisemismatch between the particular one and each of the rest of the at leastone training CI time series. The function may comprise at least one of:average, weighed average, mean, trimmed mean, median, mode, arithmeticmean, geometric mean, harmonic mean, truncated mean, generalized mean,power mean, f-mean, interquartile mean, and/or another mean.

A particular trained representative CI time series associated with aparticular known event may have a particular time duration. Theparticular trained representative CI time series of the particular timeduration may be aligned with each of the at least one training CI timeseries associated with the known event. Respective pairwise mismatch maybe computed. The particular trained representative CI time series mayminimize an aggregate mismatch with respect to the at least one trainingCI time series. The aggregate mismatch of a CI time series may be afunction of at least one pairwise mismatch between the CI time seriesand each of the at least one training CI time series aligned with the CItime series. The particular time duration may minimize the aggregatemismatch among more than one candidate time durations of the particulartrained representative CI time series. The function may comprise atleast one of: average, weighed average, mean, trimmed mean, median,mode, arithmetic mean, geometric mean, harmonic mean, truncated mean,generalized mean, power mean, f-mean, interquartile mean, and/or anothermean.

At least one of: the aggregate mismatch and/or each pairwise mismatch,may be normalized, so that the aggregate mismatch of each of the atleast one training CI time series can be compared in search of the onewith minimum aggregate mismatch. The particular time duration mayminimize the aggregate mismatch among more than one candidate timedurations of the particular trained representative CI time series.

For each of at least one candidate time duration, the optimal trainedrepresentative CI time series with the candidate time duration may becomputed. The particular time duration may be chosen as the candidatetime duration that gives minimal normalized aggregate mismatch. Thenormalized aggregated mismatch associated with a candidate time durationmay be the respective aggregate mismatch normalized by the candidatetime duration.

The particular time duration may be obtained by performing a searchamong the at least one candidate time durations with the cost functionbeing at least one of: normalized aggregate mismatch, hybrid normalizedaggregate mismatch, combination normalized aggregate match, a simplifiedmismatch, and/or another cost function. The normalized aggregatedmismatch associated with a candidate time duration may be the respectiveaggregate mismatch normalized by the candidate time duration. The searchmay comprise at least one of: brute force exhaustive search, gradientdescent, steepest descent, stochastic search, genetic search, predictivesearch, local search, multi-resolution search, hierarchical search,constrained search, unconstrained search, fast search, simplifiedsearch, and/or another search.

The particular time duration and the particular trained representativeCI time series with the particular time duration may be computediteratively. A current time duration may be initialized as one of thecandidate time durations. For the current time duration, a currentoptimal trained representative CI time series with the current timeduration may be computed. For the current optimal trained representativeCI time series with the current time duration, the current time durationmay be changed to give smaller normalized aggregate mismatch. Thecurrent time duration may be initialized with a value based on durationsassociated with the at least one training CI time series associated withthe known event. The current time duration may be initialized with avalue based on durations associated with the at least one training CItime series associated with the known event.

At least one of: the first wireless device, and/or the first targetwireless receiver, may comprise at least two antennas. Each pair offirst wireless device antenna and first target wireless receiver antennamay form a link between the first wireless device and the first targetwireless receiver. At least two links may be formed between the firstwireless device and the first target wireless receiver. Each link maycorrespond to a current CI time series.

In one embodiment, the at least one time series of training channelinformation (CI) of the wireless multipath channel may be obtained froma second wireless signal sent through the wireless multipath channel ina training phase. The wireless multipath channel may be impacted by atraining movement of a second object in the training phase. The trainingphase may be a training session, which may be carried out once,occasionally, regularly, and/or on demand.

At least one time series of first training channel information of thewireless multipath channel associated with a target positive trainingmovement of the second object in the training phase may be obtained. Thepositive training movement may be a target movement to be recognized,monitored, measured, studied, processed, detected, estimated, verified,and/or captured.

At least one time series of second training channel information of thewireless multipath channel associated with a target negative trainingmovement of the second object in the training phase may be obtained. Thenegative training movement may be a target movement to be ignored,missed, not monitored, not detected, not estimated, not recognized, notverified, not captured, not measured, and/or not studied.

At least one first quantity from the at least one time series of firsttraining channel information and/or at least one second quantity fromthe at least one time series of second training channel information maybe computed. The at least one first quantity and/or the at least onesecond quantity may comprise a motion statistics, a location statistics,a map coordinate statistics, a height statistics, a speed statistics, anacceleration statistics, a movement angle statistics, a rotationstatistics, a size statistics, a volume statistics, a time trend, a timetrend statistics, a time profile statistics, a periodic motionstatistics, a frequency statistics, a transient statistics, a breathingstatistics, a gait statistics, an action statistics, an eventstatistics, a suspicious event statistics, a dangerous event statistics,an alarming event statistics, a warning statistics, a belief statistics,a proximity statistics, a collision statistics, a power statistics, asignal statistics, a signal power statistics, a signal strengthstatistics, a received signal strength indicator (RSSI), a signalamplitude, a signal phase, a signal frequency component, a signalfrequency band component, a channel state information (CSI), a CSIstatistics, a map statistics, a time statistics, a frequency statistics,a time-frequency statistics, a decomposition statistics, a orthogonaldecomposition statistics, a non-orthogonal decomposition statistics, atracking statistics, a breathing statistics, a heartbeat statistics, abiometric statistics, a baby statistics, a patient statistics, a machinestatistics, a device statistics, a temperature statistics, a vehiclestatistics, a parking lot statistics, a venue statistics, a liftstatistics, an elevator statistics, a spatial statistics, a roadstatistics, a fluid flow statistics, a home statistics, a roomstatistics, an office statistics, a house statistics, a buildingstatistics, a warehouse statistics, a storage statistics, a systemstatistics, a ventilation statistics, a fan statistics, a pipestatistics, a duct statistics, a people statistics, a human statistics,a car statistics, a boat statistics, a truck statistics, an airplanestatistics, a drone statistics, a downtown statistics, a crowdstatistics, an impulsive event statistics, a cyclo-stationarystatistics, an environment statistics, a vibration statistics, amaterial statistics, a surface statistics, a 3-dimensional statistics, a2-dimensional statistics, a local statistics, a global statistics, apresence statistics, and/or another statistics.

The at least one threshold may be determined based on the at least onefirst quantity and/or the at least one second quantity. The at least onethreshold may be determined such that a first percentage of the firstquantity is larger than, equal to and/or less than a first threshold(not the at least one threshold). The at least one threshold may bedetermined such that a second percentage of the second quantity islarger than, equal to and/or less than a second threshold (not the atleast one threshold).

The first threshold may be greater than, equal to and/or less than thesecond threshold. The first threshold may be the second threshold. Thefirst percentage may be greater than, equal to and/or less than thesecond percentage.

The at least one time series of first training channel information ofthe wireless multipath channel may be associated with a trainingmovement of the second object inside a monitored area in the trainingphase. The target positive training movement of the second object may bethe training movement of the second object inside the monitored area.

The at least one time series of second training channel information ofthe wireless multipath channel may be associated with a trainingmovement of the second object outside the monitored area in the trainingphase. The target negative training movement of the second object may bethe training movement of the second object outside the monitored area.

The second object may be the first object. The second object may be animitation, a replacement, a backup, and/or a replica of the firstobject. The second object may be another object similar to the firstobject. The second object may be similar to the first object in terms ofstructure, size, shape, functionality, periodicity, deformationcharacteristics, motion characteristics, speed, acceleration, gait,trend, habit, wireless properties, and other characteristics.

In one embodiment, the present teaching discloses a method, apparatus,and a system for sleep monitoring. The disclosed method comprises:obtaining a time series of channel information (CI) of a wirelessmultipath channel using a processor, a memory communicatively coupledwith the processor and a set of instructions stored in the memory, andmonitoring the sleep-related motion of the user based on the time seriesof CI.

The time series of CI is extracted from a wireless signal transmittedbetween a Type 1 heterogeneous wireless device and a Type 2heterogeneous wireless device in a venue through the wireless multipathchannel. The wireless multipath channel is impacted by a sleep-relatedmotion of a user in the venue.

Monitoring the sleep-related motion comprises monitoring at least one ofthe following of the user: sleep timings, sleep durations, sleep stages,sleep quality, sleep apnea, sleep problems, sleep disorders, breathingproblems, gasping, choking, teeth-grinding, pause of sleep, absence ofsleep, insomnia, restlessness during sleep, hypersomnia, parasomnia,day-time sleepiness, sleep locations, sleep-while-driving, sleepdisruptions, nightmares, night terrors, sleep walking, REM sleepbehavior disorder, Circadian rhythm disorder, non-24-hour sleep-wakedisorder, periodic limb movement disorder, shift-work sleep disorder,narcolepsy, confusional arousals, sleep paralysis, another sleep-relatedcondition, and/or another sleep-related behavior.

Sleep timings comprises timings of at least one of: go-to-bed,sleep-onset, wake-up, REM-onset, NREM-onset, onset of sleep stagetransitions, sleep disorders, sleep problems, breathing problems,insomnia, hypersomnia, parasomnia, sleep hypnogram-related events, sleepdisruptions, sleep apnea, snoring during sleep, sleeping-not-on-a-bed,day-time sleep, sleep-walking, sleep-related events, sleep-relatedcondition, and/or, sleep-related behavior, etc.

Sleep stages comprises at least one of: wake-up, rapid-eye-movement(REM) and/or non-REM (NREM).

At least one of: a time function of breathing rate, and a time functionof motion statistics, of the user may be computed based on the series ofCI. If breathing is not detected at time t, the breathing rate at time tmay be computed as zero. The sleep-related motion of the user may bemonitored based on at least one of: the time function of breathing rate,and/or the time function of motion statistics, of the user.

At least one of: a time function of breathing ratio, and a time functionof motion ratio, of the user may be computed based on the series of CI.The breathing ratio at time t may be computed as percentage of time whenthe time function of breathing rate is non-zero in a first time windowcomprising the time t. The motion ratio at time t may be computed aspercentage of time when the time function of motion statistics is largerthan a first threshold within a second time window comprising the timet. The sleep-related motion of the user may be monitored based on atleast one of: the time function of breathing ratio, and/or the timefunction of motion ratio, of the user.

A sleep stage may be classified as “awake” if at least one of: themotion ratio is greater than a second threshold, and/or the breathingratio is less than a third threshold. The sleep stage may be classifiedas “asleep” if at least one of: the motion ratio is less than the secondthreshold, and/or the breathing ratio is greater than the thirdthreshold. The “asleep” stage may comprise at least one of:rapid-eye-movement (REM) stage, and/or non-REM (NREM) stage.

A breathing rate trend function may be computed by low-pass filteringthe time function of breathing rate. A detrended breathing rate functionmay be computed by subtracting the breathing rate trend function fromthe time function of breathing rate. A time function of breathing ratevariance may be computed by computing variance of the detrendedbreathing rate function within a sliding time window. The sleep-relatedmotion of the user may be monitored based on the time function ofbreathing rate variance.

An average NREM breathing rate may be computed by identifying a peak ofa histogram of the time function of breathing rate in “asleep” stage inan overnight period. (e.g. the whole night, or the whole nightsubtracting any “awake” periods). A time function of breathing ratedeviation may be computed by computing a distance between the averageNREM and a percentile of the breathing rate within a sliding timewindow. The sleep stage may be classified as at least one of: REM stageand/or NREM stage, based on the time function of breathing ratedeviation.

A time function of breathing rate variance may be computed by computingvariance of a detrended breathing rate function within a first slidingtime window. A time function of breathing rate deviation may be computedby computing a distance between an average NREM and a percentile of thebreathing rate within a second sliding time window. The sleep stage maybe classified as at least one of: REM stage, and/or NREM stage, based onthe time function of breathing rate variance and the time function ofbreathing rate deviation.

A classifier may be trained based on at least one of: breathing ratevariance, and breathing rate deviation, using machine learning. Themachine learning may comprise at least one of: supervised learning,unsupervised learning, semi-supervised learning, active learning,reinforcement learning, support vector machine, deep learning, featurelearning, clustering, regression, and/or dimensionality reduction. Thesleep stage may be classified as at least one of: REM stage, and/or NREMstage, based on the classifier.

A quantity related to the sleep-related motion of the user may becomputed based on the time series of CI. The sleep-related motion of theuser may be monitored based on the quantity.

The quantity may comprise at least one of: the time the user goes tobed, the time the user gets out of bed, the sleep onset time, total timeit takes the user to fall asleep, the wake up time, sleep disruptiontime, number of sleep disruption period, mean disruption duration,variance of disruption duration, total time in bed, total time the useris asleep, time periods of REM, time periods of NREM, time periods ofawake, total time of REM, total time of NREM, number of REM periods,number of NREM periods, time of toss and turn in bed, duration oftossing and turning, hypnogram, periods of apnea, periods of snore,total duration of apnea, number of apnea periods, average duration ofapnea period, periods of breathing problems, sleep quality score,daytime sleep, time periods of daytime sleep, total duration of daytimesleep, number of period of daytime sleep, average duration of period ofdaytime sleep, and another quantity.

FIG. 4 summarizes a proposed scheme for breathing signal extraction andmaximization. The left part of FIG. 4 shows the decomposition of themeasured ACF of the channel power response when a person breathesnormally in the monitored area, and the right part shows the MRC schemefor boosting the SNR of the ACF of the breathing signal. FIG. 5 depictsan illustrative example based on real-world measurements, where the SNRof the breathing signal is amplified by 2.5 dB compared to the bestsubcarrier indicated by largest variance and by 3.7 dB compared todirectly averaging all subcarriers. FIG. 6 further demonstrates thegains of the disclosed ACF-based MRC scheme and confirms theobservations herein that amplitudes and their variances are noteffective metrics for subcarrier selection. As seen, the subcarrier thatis the most sensitive to motion (i.e., holding the largest motionstatistic) could experience very small amplitude and low variance.

Sleep Stage Recognition

SMARS divides the continuous motion and breathing estimates of overnightsleep into 300-second epochs. For each epoch, SMARS recognizes threedifferent sleep stages, i.e., wake, REM sleep and NREM sleep. Thestaging is performed in two steps: First, SMARS differentiates wake fromsleep mainly by body motions; Second, REM and NREM stages are furtheridentified during sleep period.

Sleep/Wake Detection. SMARS first implements a sleep-wake detector toidentify the sleep and wake states. The key insight is that, morefrequent body movements will be observed when a subject is awake, whilemainly breathing motion presents when he/she is asleep. Since bodilymovements are significantly stronger than breathing motions, and both ofthem can be easily captured and quantified by the motion statisticdefined herein, SMARS utilizes it to distinguish between sleep and wakestates.

Specifically, one can define motion ratio as the percentage of time whenthe motion statistic, {circumflex over (ρ)}_(b)(1/F_(s)), is larger thana preset threshold. Thus for the wake state, a higher motion ratio isexpected, as shown in FIG. 7A. Similarly, one can also define breathingratio as the percentage of time when the breathing signal is detected.Since bodily movements destroy the periodicity of the environmentaldynamics, the breathing ratio will be lower when a subject is awake, asshown in FIG. 7B.

Combining the above two features, SMARS identifies an epoch as sleeponly when the motion ratio is smaller than the predefined threshold andthe breathing ratio is larger than the other threshold. Both thresholdsare empirically determined as in FIG. 7A and FIG. 7B. Since thedisclosed model statistically considers all multipaths indoors, thevalues of both thresholds generalize to different environments andsubjects.

REM/NREM Recognition. SMARS exploits the following clinical facts andaccordingly extracts two distinctive features from breathing rateestimates for REM/NREM stages classification: Breathing rate is usuallyfaster and presents higher variability and irregular patterns for REMstage, while more stable and slower for NREM stage.

Since NREM stage constitutes the majority (about 75% to 80%) of totalsleep for typical healthy adults, the average breathing rate during NREMstage can be estimated by localizing the peak of the histogram ofovernight breathing rate estimates, as shown in FIG. 8A. On this basis,one can define breathing rate deviation, the distance between theestimated average NREM breathing rate and the 90% tile of the breathingrate for each epoch, to quantify the deviation of the breathing rateduring REM stage from that during NREM stage.

To extract the variability of the breathing rate for each epoch, one canfirst estimate the trend of breathing rate by applying a low pass filterto the breathing estimates of the whole night, and obtain the detrendedbreathing rate estimates by subtracting the trend from the originalbreathing rate estimates. Then, the breathing rate variability isdefined and calculated for each epoch as the variance of the detrendedestimates normalized by the length of epoch.

FIG. 8B visualizes the distribution of the proposed two features underNREM and REM sleep, respectively. As one can see from FIG. 8B, themajority of the breathing rate variability and breathing rate deviationof NREM sleep are much smaller than those of REM sleep. Based on thesetwo features, one can train a support vector machine (SVM), a widelyused binary classifier, to differentiate between REM and NREM sleep.

Sleep Quality Assessment

When one obtains the estimates of wake, REM, and NREM stages of a wholesleep, one can assess the elusive sleep quality for a user by followingstandard approach used in clinical practice. In particular, one cancalculate the sleep score for each night based on the recognized sleepstages as follows. Let T_(N), T_(R) and T_(W) denote the durations(measured in hours) of NREM sleep, REM sleep and wake, respectively.Since there is no standard formula for sleep score calculation, a simpleformula for the sleep score is applied in SMARS:S=10*T _(N)+20*T _(R)−10*T _(W),which means that the more you sleep, the more you have REM sleep, theless you keep awake in the bed, the better your sleep score is.According to recent research, REM sleep is crucial for mental recovery,and thus a higher weight has been assigned to REM sleep.

SMARS envisions a practical sleep monitoring for daily in-home use.Although it does not make much sense to compare the sleep score amongdifferent users, the trend or history of the sleep score for aparticular user would reflect the changes of his/her sleep quality. Suchresults provide clinically meaningful evidences to help diagnose sleepdisorders and manage personal health, in an attractive way. FIG. 9illustrates an exemplary network environment for sleep monitoring,according to one embodiment of the present teaching. FIG. 10 illustratesan exemplary algorithm design for sleep monitoring, according to oneembodiment of the present teaching.

In the era of Internet of Things (IoT), smart appliances are designedand developed to achieve customer satisfaction and convenience and themarket for smart appliances is primed. For instance, a Smart TV candeliver an innovative TV usage pattern. Instead of using theconventional remote controller to control a TV at home, with the help ofwireless sensing, the Smart TV can be automatically turned on/off,paused and/or resumed by detecting certain motion patterns in front ofthe TV and by sensing the presence of a human in a certain area, e.g.,the living room. In one embodiment, the present teaching disclosesmonitoring the presence of a live object and potential motion of theobject based on time-reversal technology in a rich-scatteringenvironment, e.g. an indoor environment or urban metropolitan area,enclosed environment, underground environment, etc.

As shown in FIG. 11, hang on the wall of the living room, the Smart TVhas one Origin and one Bot, i.e., two WiFi transceivers, embedded insideof it to sense the wireless propagation environment of the living room.When the user is pacing around in front of the TV within a certaindistance, e.g. 2 to 3 feet from the TV, the TV will detect it by sensingthe environment with the Origin and the Bot and capturing and analyzingthe channel state information (CSI), and will finally automatically turnon. When the user is watching the TV and sitting on the sofa, even itmight be far away from the TV, the Origin and the Bot can still capturethe perturbation introduced to the CSI by the motion or vital signals ofthe present user. Only when no one is in the living room, thepropagation environment will be quiet and the CSI will be consistentalong the time and can be sensed by the Origin and the Bot inside theSmart TV. In other words, with the help of the Origin and the Bot insidethe Smart TV, the Smart TV can sense the indoor environment wirelesslyand differentiate between different indoor states: (1) when the user ispacing close to the TV within the target area (2 to 3 feet), (2) whenthe user is sitting on the sofa and watching TV (i.e., daily activityinside the living room), and (3) when the room is empty and no one isthere.

In the designed scheme of one embodiment, the Smart TV will turn on andoff automatically as a response to the three detected indoor states.When TV is on (or off) and state (1) is detected, the TV will be turnedoff (or on) immediately. When the TV is on and state (2) is detected,the TV will remain as on. When the TV is on and state (3) is detectedfor a certain time period, the TV will enter into a countdown mode.During the count-down period and before the countdown limit is reached,if the state (2) is detected, the TV will remain on and the count-downmode is reset and disabled. However, if the state (3) persists duringthe count-down period, the TV will be shut down when the countdown limitis reached.

Methodology:

In the Smart TV, the Bot keeps transmitting channel probing signals tothe Origin at a given sounding rate 1/Ts where Ts is the channel probinginterval in seconds. Based on each received channel probing signal, theOrigin can estimate the channel state information (CSI). For every 1/TsCSI, a motion statistic value (metric) is derived as the averagedauto-correlation value between adjacent CSIs for a total of Mconsecutive CSIs. Due to the nature of multipath propagation, the CSIwill be disturbed when users are inside the room and differentactivities will result in a different motion pattern in the CSIs. Whenthe motion is close to the Smart TV (the Origin and the Bot), a largermotion statistic value will be produced than the one associated to whenthe motion is away from the TV. When there is no motion inside the room,the corresponding motion statistics will be very small, e.g. around 0.Hence, by using motion statistics, the aforementioned 3 indoor statescan be categorized well.

Algorithm:

Different algorithms are disclosed to guarantee the accuracy as well asthe robustness of the proposed Smart TV system.

A) To detect state (1) when the user is pacing in front of the TV, afirst-in-first-out buffer B_1 with a fixed length W_1 is used to storethe latest motion statistics calculated from incoming CSIs. The medianvalue X_1 of all elements in buffer B_1 is used as the metric. X_1 keepsupdating and is compared with a predefined threshold R_1. When X_1>R_1,it is determined that state (1) is detected and the Smart TV will beturned on (or off) if its current state is off (or on).

B) Meanwhile, to detect state (3) when no one is inside the room,another first-in-first-out buffer B_2 with a fixed length W_2 is used tostore the latest motion statistics calculated from incoming CSIs. Themedian value X_2 of all elements in buffer B_2 is used as the metric.X_2 keeps being updated and is compared with a predefined threshold R_2.When X_2<R_2, it is determined that state (3) is detected and the SmartTV will enter the countdown mode with the countdown limit being T_0.During the countdown mode and before the limit T_0 is reached, theOrigin and the Bot keeps sensing the indoor propagation environment andupdating the buffer B_1 and B_2 with latest motion statistics.Meanwhile, a new first-in first-out motion statistics buffer B_3 isopened with length W_3 and it is aimed to detect if the state (2) or anymotion happens during this period. The median value X_3 of all elementsin buffer B_3 is used as the metric and compared with a predefinedthreshold R_3. When X_3>R_3, it is determined that state (2) isdetected, i.e., there is motion or the user present in the room. Duringthe countdown period, if the state (2) is detected, the countdown willbe terminated and the Smart TV will not be shut down. Otherwise, theSmart TV will automatically shut-down when the countdown limit isreached.

C) The length of each buffer is adjustable. Typically, W_1 is 5 seconds,W_2 is 15 seconds, W_3 is 5 seconds and T_0 is 30 seconds. The thresholdof R_1, R_2, and R_3 can be adjusted manually or learned through atraining process during the initial set-up.

The Smart TV disclosed herein may display any video on a TV screen. Inone embodiment, the video can be paused and/or resumed automatically asa response to the three detected indoor states: (1) when the user ispacing close to the TV within the target area (2 to 3 feet), (2) whenthe user is sitting on the sofa and watching TV (i.e., daily activityinside the living room), and (3) when the room is empty and no one isthere. When the TV is on and playing any video on the screen, if thestate (1) is detected, the TV will immediately pause and start acountdown period T_1. During the countdown period of T_1, if the state(1) is detected again, the TV will resume and start to play from thepaused scene. If state (1) is never detected while the state (2) isdetected, the TV will remain paused with its screen being on and showingthe paused scene. However, if the state (3) keeps being detected duringthe countdown period of T_1, i.e. none of the state (1) or the state (2)has been detected before the countdown limit T_1 is reached, the TV willenter the sleep mode with the display being off and start anothercountdown period T_2. During the countdown period of T_2, if the state(1) is detected, the TV will immediately light up and start to play fromthe paused scene. If the state (2) is detected instead of the state (1),the TV will immediately light up, stay in the pause mode, and show thepaused screen. However, if before the limit T_2 is reached, none of thestate (1) or the state (2) has ever been detected, i.e., the TV detectsthe state (3) all the time, then the TV which is in the sleep mode willturn off automatically, but remember the last scene before it is paused.

Algorithm:

Different algorithms are disclosed to guarantee the accuracy as well asthe robustness of the proposed Smart TV system.

A) To detect the state (1) when the user is pacing in front of the TV, afirst-in-first-out buffer B_1 with a fixed length W_1 is used to storethe latest motion statistics calculated from incoming CSIs. The medianvalue X_1 of all elements in buffer B_1 is used as the metric. X_1 keepsupdating and is compared with a predefined threshold R_1. When X_1>R_1,it is determined that state (1) is detected and the Smart TV will bepaused or resumed given its current state. If the current state of theSmart TV is being paused, the Smart TV will immediately resume to playfrom the paused scene. However, if the Smart TV was playing a movie andit is being paused as a response to the detection of the state (1), acountdown mode of limit T_1 will be started immediately to decide if theSmart TV enter the sleep mode or not.

B) Meanwhile, to detect the state (3) when no one is inside the room,another first-in-first-out buffer B_2 with a fixed length W_2 is used tostore the latest motion statistics calculated from incoming CSIs. Themedian value X_2 of all elements in buffer B_2 is used as the metric.X_2 keeps being updated and is compared with a predefined threshold R_2.When X_2<R_2, it is determined that state (3) is detected.

C) Moreover, a first-in first-out motion statistics buffer B_3 is openedwith length W_3 and it is aimed to detect if the state (2) or any motionhappens during this period. The median value X_3 of all elements inbuffer B_3 is used as the metric and compared with a predefinedthreshold R_3. When X_3>R_3, it is determined that state (2) isdetected, i.e., there is motion or the user present in the room.

D) During the countdown of T_1 and before the limit T_1 is reached, theOrigin and the Bot keeps sensing the indoor propagation environment andupdating the buffer B_1, B_2, and B_3 with latest motion statistics. Ifthe state (1) is detected before the limit T_1 is reached, the Smart TVwill immediately resume and continue to play from the paused scene, andthe countdown will be terminated. If the state (1) is not detected butthe state (2) is detected before the limit T_1 is reached, the countdownof T_1 will be reset and started over again while the Smart TV remainsin the paused mode. On the other hand, if none of the state (1) or thestate (2) is detected, i.e., the Origin and the Bot in the Smart TVkeeps detecting state (3), the TV will enter the sleep mode with thedisplay being off when the countdown limit T_1 is reached.

E) As soon as the Smart TV enters the sleep mode, a new countdown oflimit T_2 starts. During the countdown of T_2, the Smart TV keeps itsdisplay off until any of the following three cases happens. If the state(1) is detected, the countdown of T_2 will be terminated and the SmartTV will light up and resume to play from the paused scene immediately.If the state (2) is detected, the countdown of T_2 will be terminatedwhile the countdown of T_1 will start over again. Meanwhile, the SmartTV will light up, remain paused with the display showing the pausedscene. If the state (3) keeps being detected, i.e., none of the state(1) or the state (2) is detected, the Smart TV in the sleep mode will beturned off automatically at the time when the countdown limit T_2 isreached.

C) The length of each buffer is adjustable. The threshold of R_1, R_2,and R_3 can be adjusted manually or learned through a training processduring the initial set-up.

Experimental Results:

To validate and demonstrate the idea of Smart TV, experiments areconducted and the set-up is shown in FIG. 12 where the Origin and theBot are put at the left and right boundary of the TV, right under theTV. The space in front of the test TV is partitioned into 6 zones asshown in FIG. 13 and the Zone 1 which is within 1 meter from the TV isconsidered as the intended area of the state (1).

The CSI between the Origin and the Bot can be collected to calculate themotion statistics for cases when one tester is pacing in each zone for 1minute and for the case when the room is empty for 1 minute. Thedistributions of statistics corresponding to different scenarios areplotted as the cumulative distribution function (CDF), where the legend“CX” means the scenario of user pacing in Zone X. It is clear thatalmost 90% of the statistics for an empty room are below 0, while almost90% of statistics of pacing in Zone 1 are above 0.6. For other motioninside the room, most of the motion statistics fall into the range of0.1 to 0.5. Hence, with the help of motion statistics, the proposedSmart TV is capable of differentiating between those 3 indoor states.

Based on motion statistics and the system output (turn-on detection)along the time for the user pacing at different zones, when the turn-ondetection is one, the Smart TV will be turned on (off) automatically.100% of detection is achieved for Zone 1, i.e. the intended triggerzone, and 0 false alarm for other zones.

Based on motion statistics and the system output (activity detection)along the time for both the scenarios of an empty room and someonesitting in Zone 4, when the activity detection is 1, the TV will not beturned off. When the activity detection is 0, the TV will be turned offafter the countdown. The motion statistics may be sensitive and capableof detecting tiny and distant motion, while maintaining its robustnessfor an empty room.

Based on results of 5 turn-on tests and 5 passing-by tests, as the useris intentionally pacing in Zone 1 to activate the state (1), theproposed Smart TV system can always capture and respond to it quicklyand accurately. When the user is only walking through Zone 1 randomlywithout an intention to activate the state (1), the proposed Smart TVsystem will never produce false alarm which demonstrates its robustness.

Potential Use Cases:

By utilizing the CSI that characterizes the indoor environment to detectand distinguish different indoor environments, the disclosed Smart TVsystem is intelligent and capable of turning on and off the TVautomatically based on the indoor states of when no one is in the room,when the user is within the room performing daily activities and/or whenthe user is pacing in front of the TV. The disclosed Smart TV system candetect motion in close proximity, and quickly respond to it. Meanwhile,the disclosed system can also be extended to other smart appliances,such as the refrigerator, the electric fireplace, the display screen foradvertising and so on.

FIG. 14 illustrates another exemplary setting of Smart TV where theOrigin and/or Bot may be integrated into the TV for presence detection.FIG. 15 illustrates another exemplary setting of Smart TV where theOrigin and/or Bot may be installed in a speaker placed in front of theTV for presence detection. FIG. 16 illustrates another exemplary settingof Smart TV, with TV placed on a table, and Origin/Bot installed on thetable. The table can be computer furniture to house the computer, orentertainment center to house the TV.

FIG. 17 illustrates another scenario of a smart fan. The fan may be astanding fan with a high stand and the Origin and/or Bot may beinstalled on the high stand. When a person is detected, either throughmotion detection or vital signs (such as breathing) detection, the fancan be turned on.

FIG. 18 illustrates another scenario of a smart car. The Origin and Botmay be installed on the outside of a car. When human presence isdetected, the car may do something: e.g. activate security; check ID ofthe person; if the user is confirmed, the car may open the door, trunk,and etc. or start engine (warm up) or start AC to cool the car.

FIG. 19 illustrates an exemplary interior bird-view of a car for seatoccupancy detection and people counting, according to one embodiment ofthe present teaching. In an example, one or more Type 1 devices andmultiple Type 2 devices (e.g. N=4) may be placed in a confined area(e.g. closed area such as a car, a conference room, a bus, an airplane,or a cinema, semi-open area such as a bus with windows open, or abalcony with 8 outdoor chairs around an outdoor table) with multiple“seats” at fixed locations (may be a space for standing, sitting,kneeling, lying down, etc.) that may hold a person. A presence of aperson at one or more “seats” may be detected based on time series of CIextracted from wireless signals sent from the Type 1 devices to the Type2 devices.

FIG. 19 shows a particular example of a car with 4 seats, two in frontrow and two in back row. Note that each seat has a “seat bed” for aperson to sit on and a “seat back” for a person to lean back on. TheType 1 device may be placed in the front, on the dash board. Four Type 2devices may be deployed, one at each of the 4 seats (e.g. in/on/underthe seat bed, or in/on/under the seat bed). When a seat A (e.g. driverseat, or right seat on front row, or back row left seat, etc.) isoccupied by the driver or a passenger or a baby in a car-seat, the CI(channel information) associated with the occupied seat A may behavedifferently (e.g. become smaller or bigger) from the CI associated withan empty seat A. Thus one can detect the seat occupancy of each seat byexamining the CI. By performing such test for all the seats, one cancount the amount of people in the car. If a person sits at non-standardlocations (e.g. between two seats, in the center of back row, in thecenter of front row, or a baby in a car seat), CI associated multipleType 2 devices can be analyzed jointly to determine if there is a personthere. As signature of a baby in car-seat may be different from an adultor a children, adult/children/baby classification may be performed basedon the CI.

A task may be performed based on the seat occupancy described above. Forexample, the task may be to arm an air-bag if a seat is occupied, butdisarm the airbag if the seat is not occupied. If a small size person(e.g. a child) instead of a regular-size adult is detected, an air-bagdesigned for adults may not be armed. The heating/air-condition settingmay be adjusted. The task may be to control windows, lighting, audiosystem, entertainment system (e.g. video), noise cancelling,shock-absorbing system, stabilizing size, car avoidance system, safetyfeatures, tire pressure, any other car subsystems, etc. For example, ifa passenger is detected at the front right seat, the temperature at thatregion (front, right) may be controlled to a preset level. If the seatis empty, the temperature may be adjusted differently.

FIGS. 20A-20D illustrate changes of channel information (CI) accordingto various seat occupancy situations in a car, according to oneembodiment of the present teaching. FIG. 20A shows CI when no seat isoccupied. FIG. 20B shows CI when seat 1 (e.g. seat with Bot antenna 1 inFIG. 19) is occupied. FIG. 20C shows CI when seat 3 (e.g. seat with Botantenna 3 in FIG. 19) is occupied. FIG. 20D shows CI when both seat 1and seat 3 are occupied.

The features described above may be implemented advantageously in one ormore computer programs that are executable on a programmable systemincluding at least one programmable processor coupled to receive dataand instructions from, and to transmit data and instructions to, a datastorage system, at least one input device, and at least one outputdevice. A computer program is a set of instructions that may be used,directly or indirectly, in a computer to perform a certain activity orbring about a certain result. A computer program may be written in anyform of programming language (e.g., C, Java), including compiled orinterpreted languages, and it may be deployed in any form, including asa stand-alone program or as a module, component, subroutine, abrowser-based web application, or other unit suitable for use in acomputing environment.

Suitable processors for the execution of a program of instructionsinclude, e.g., both general and special purpose microprocessors, digitalsignal processors, and the sole processor or one of multiple processorsor cores, of any kind of computer. Generally, a processor will receiveinstructions and data from a read-only memory or a random access memoryor both. The essential elements of a computer are a processor forexecuting instructions and one or more memories for storing instructionsand data. Generally, a computer will also include, or be operativelycoupled to communicate with, one or more mass storage devices forstoring data files; such devices include magnetic disks, such asinternal hard disks and removable disks; magneto-optical disks; andoptical disks. Storage devices suitable for tangibly embodying computerprogram instructions and data include all forms of non-volatile memory,including by way of example semiconductor memory devices, such as EPROM,EEPROM, and flash memory devices; magnetic disks such as internal harddisks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROMdisks. The processor and the memory may be supplemented by, orincorporated in, ASICs (application-specific integrated circuits).

While the present teaching contains many specific implementationdetails, these should not be construed as limitations on the scope ofthe present teaching or of what may be claimed, but rather asdescriptions of features specific to particular embodiments of thepresent teaching. Certain features that are described in thisspecification in the context of separate embodiments may also beimplemented in combination in a single embodiment. Conversely, variousfeatures that are described in the context of a single embodiment mayalso be implemented in multiple embodiments separately or in anysuitable sub-combination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems maygenerally be integrated together in a single software product orpackaged into multiple software products.

Particular embodiments of the subject matter have been described. Anycombination of the features and architectures described above isintended to be within the scope of the following claims. Otherembodiments are also within the scope of the following claims. In somecases, the actions recited in the claims may be performed in a differentorder and still achieve desirable results. In addition, the processesdepicted in the accompanying figures do not necessarily require theparticular order shown, or sequential order, to achieve desirableresults. In certain implementations, multitasking and parallelprocessing may be advantageous.

We claim:
 1. A method implemented on a machine having a processor, amemory communicatively coupled with the processor and a set ofinstructions stored in the memory for recognizing an event, comprising:for each of at least one known event happening in a venue in arespective training time period: transmitting, by an antenna of a firsttransmitter, a respective training wireless signal to at least one firstreceiver through a wireless multipath channel impacted by the knownevent in the venue in the training time period associated with the knownevent, obtaining, asynchronously by each of the at least one firstreceiver based on the training wireless signal, at least one time seriesof training channel information (training CI time series) of thewireless multipath channel between the first receiver and the firsttransmitter in the training time period associated with the known event,and pre-processing the at least one training CI time series; training aprojection for CI using a dimension reduction method based on thetraining CI time series associated with the at least one known event,training at least one classifier for the at least one known event basedon the at least one training CI time series and the projection; and fora current event happening in the venue in a current time period,transmitting, by an antenna of a second transmitter, a current wirelesssignal to at least one second receiver through the wireless multipathchannel impacted by the current event in the venue in the current timeperiod associated with the current event, obtaining, asynchronously byeach of the at least one second receiver based on the current wirelesssignal, at least one time series of current channel information (currentCI time series) of the wireless multipath channel between the secondreceiver and the second transmitter in the current time periodassociated with the current event, pre-processing the at least onecurrent CI time series, and applying the at least one classifier to:classify, based on the projection, at least one of: the at least onecurrent CI time series, a portion of a particular current CI timeseries, and a combination of the portion of the particular current CItime series and a portion of an additional CI time series, and associatethe current event with at least one of: a known event, an unknown eventand another event, wherein a training CI time series associated with afirst receiver and a current CI time series associated with a secondreceiver have at least one of: different starting times, different timedurations, different stopping times, different counts of items in theirrespective time series, different sampling frequencies, differentsampling periods between two consecutive items in their respective timeseries, and channel information (CI) with different features.
 2. Themethod of claim 1, further comprising: aligning a first section of afirst time duration of a first CI time series and a second section of asecond time duration of a second CI time series, and determining amapping between items of the first section and items of the secondsection.
 3. The method of claim 2, wherein: the first CI time series isprocessed by a first operation; the second CI time series is processedby a second operation; and at least one of the first operation and thesecond operation comprises at least one of: subsampling, re-sampling,interpolation, filtering, transformation, feature extraction, andpre-processing.
 4. The method of claim 2, further comprising mapping afirst item of the first section to a second item of the second section,wherein at least one constraint is applied on at least one function ofat least one of: the first item of the first section of the first CItime series; another item of the first CI time series; a time stamp ofthe first item; a time difference of the first item; a time differentialof the first item; a neighboring time stamp of the first item; anothertime stamp associated with the first item; the second item of the secondsection of the second CI time series; another item of the second CI timeseries; a time stamp of the second item; a time difference of the seconditem; a time differential of the second item; a neighboring time stampof the second item; and another time stamp associated with the seconditem.
 5. The method of claim 4, wherein one of the at least oneconstraint is that a difference between the time stamp of the first itemand the time stamp of the second item is upper-bounded by an adaptiveupper threshold and lower-bounded by an adaptive lower threshold.
 6. Themethod of claim 1, further comprising: determining a section of a timeduration of a CI time series adaptively; and determining a starting timeand an ending time of the section, wherein determining the sectioncomprises: computing a tentative section of the CI time series, anddetermining the section by removing a beginning portion and an endingportion of the tentative section.
 7. The method of claim 6, whereindetermining the section further comprises: determining the beginningportion of the tentative section by: considering items of the tentativesection with increasing time stamp as a current item iteratively, oneitem at a time, computing recursively an activity measure associatedwith at least one of: the current item associated with a current timestamp, past items of the tentative section with time stamps not largerthan the current time stamp, and future items of the tentative sectionwith time stamps not smaller than the current time stamp, adding thecurrent item to the beginning portion of the tentative section when afirst criterion associated with the activity measure is satisfied; anddetermining the ending portion of the tentative section by: consideringitems of the tentative section with decreasing time stamp as a currentitem iteratively, one item at a time, iteratively computing anddetermining at least one activity measure associated with at least oneof: the current item associated with a current time stamp, past items ofthe tentative section with time stamps not larger than the current timestamp, and future items of the tentative section with time stamps notsmaller than the current time stamp, adding the current item to theending portion of the tentative section when a second criterionassociated with the at least one activity measure is satisfied.
 8. Themethod of claim 7, wherein: at least one of the first criterion and thesecond criterion comprises at least one of: the activity measure issmaller than an adaptive upper threshold, the activity measure is largerthan an adaptive lower threshold, the activity measure is smaller thanan adaptive upper threshold consecutively for at least a predeterminedamount of consecutive time stamps, the activity measure is larger thanan adaptive lower threshold consecutively for at least an additionalpredetermined amount of consecutive time stamps, the activity measure issmaller than an adaptive upper threshold consecutively for at least apredetermined percentage of the predetermined amount of consecutive timestamps, the activity measure is larger than an adaptive lower thresholdconsecutively for at least another predetermined percentage of theadditional predetermined amount of consecutive time stamps, anotheractivity measure associated with another time stamp associated with thecurrent time stamp is smaller than another adaptive upper threshold andlarger than another adaptive lower threshold, at least one activitymeasure associated with at least one respective time stamp associatedwith the current time stamp is smaller than respective upper thresholdand larger than respective lower threshold, and a percentage of timestamps with associated activity measure smaller than respective upperthreshold and larger than respective lower threshold in a set of timestamps associated with the current time stamp exceeds a threshold; andthe activity measure associated with an item at time T1 comprises atleast one of: a first function of the item at time T1 and an item attime T1−D1, wherein D1 is a pre-determined positive quantity, a secondfunction of the item at time T1 and an item at time T1+D1, a thirdfunction of the item at time T1 and an item at time T2, wherein T2 is apre-determined quantity, and a fourth function of the item at time T1and another item.
 9. The method of claim 8, wherein: at least one of:the first function, the second function, the third function, and thefourth function, is at least one of: a function F1(x, y, . . . ) with atleast two scalar arguments: x and y, a function F2(X, Y, . . . ) with atleast two vector arguments: X and Y, and a function F3(X1, Y1, . . . )with at least two arguments: X1 and Y1; the function F1 is a function ofat least one of the following: x, y, (x−y), (y−x), abs(x−y), x^a1, y^b1,abs(x^a1−y^b1), (x−y)^a1, (x/y), (x+a1)/(y+b1), (x^a1/y^b1), and((x/y)^a1−b1), wherein a1 and b1 are predetermined quantities; both Xand Y are n-tuples such that X=(x_1, x_2, . . . , x_n) and Y=(y_1, y_2,. . . , y_n); the function F2 is a function of at least one of thefollowing: x_i, y_i, (x_i−y_i), (y_i−x_i), abs(x_i−y_i), x_i ^a2, y_i^b2, abs(x_i^a2−y_i ^b2), (x_i−y_i)^a2, (x_i/y_i), (x_i+a2)/(y_i+b2),(x_i ^a2/y_i ^b2), and ((x_i/y_i)^a2−b2); i, ranging from 1 to n, is acomponent index of the n-tuples X and Y; both X1 and Y1 are n-tuplescomprising N components such that X1=(x1_1, x1_2, . . . , x1_N) andY1=(y1_1, y1_2, . . . , y1_N); the function F3 comprises acomponent-by-component summation of another function of at least one ofthe following: x1_j, y1_j, (x1_j−y1_j), (y1_j−x1_j), abs(x1_j−y1_j),x1_j^a3, y1_j^b3, abs(x1_j^a3−y1_j ^b3), (x1_j−y1_j)^a3, (x1_j/y1_j),(x1_j+a3)/(y1_j+b3), (x1_j^a3/y1_j ^b3), and ((x1_j/y1_j)^a3−b3); and j,ranging from 1 to N, is a component index of the n-tuples X1 and Y1. 10.The method of claim 2, further comprising computing the mapping usingdynamic time warping (DTW), wherein the DTW comprises a constraint on atleast one of: the mapping, the items of the first CI time series, theitems of the second CI time series, the first time duration, the secondtime duration, the first section, and the second section.
 11. The methodof claim 1, further comprising: aligning a first section of a first timeduration of a first CI time series and a second section of a second timeduration of a second CI time series; computing a map comprising aplurality of links between first items of the first section and seconditems of the second section, wherein each of the plurality of linksassociates a first item with a first time stamp with a second item witha second time stamp; computing a mismatch cost between the aligned firstsection and the aligned second section; applying the at least oneclassifier based on the mismatch cost, wherein: the mismatch costcomprises at least one of: an inner product, an inner-product-likequantity, a quantity based on correlation, a quantity based oncovariance, a discriminating score, a distance, a Euclidean distance, anabsolute distance, an L_1 distance, an L_2 distance, an L_k distance, aweighted distance, a distance-like quantity and another similarityvalue, between the first vector and the second vector, and a functionof: an item-wise cost between a first item of the first section of thefirst CI time series and a second item of the second section of thesecond CI time series associated with the first item by a link of themap, and a link-wise cost associated with the link of the map, thealigned first section and the aligned second section are representedrespectively as a first vector and a second vector that have a samevector length, and the mismatch cost is normalized by the same vectorlength.
 12. The method of claim 11, further comprising: applying the atleast one classifier to a plurality of first sections of the first CItime series and a plurality of respective second sections of the secondCI time series; obtaining at least one tentative classification result,each tentative classification result being associated with a respectivefirst section and a respective second section; and associating thecurrent event with at least one of: the known event, the unknown eventand the another event, based on a largest number of the at least onetentative classification result associated with at least one of: theknown event, the unknown event and the another event.
 13. The method ofclaim 1, wherein the dimension reduction method comprises at least oneof: principal component analysis (PCA), PCA with different kernel,independent component analysis (ICA), Fisher linear discriminant, vectorquantization, supervised learning, unsupervised learning,self-organizing maps, auto-encoder, neural network, and deep neuralnetwork.
 14. The method of claim 1, further comprising: re-training theprojection using at least one of: the dimension reduction method, and anadditional dimension reduction method, based on at least one of: theprojection before the re-training, the training CI time series, at leastone current CI time series obtained as of the re-training of theprojection, and additional training CI time series, wherein theadditional dimension reduction method comprises at least one of: asimplified dimension reduction method, principal component analysis(PCA), PCA with different kernels, independent component analysis (ICA),Fisher linear discriminant, vector quantization, supervised learning,unsupervised learning, self-organizing maps, auto-encoder, neuralnetwork, and deep neural network; re-training the at least oneclassifier based on at least one of: the re-trained projection, thetraining CI time series associated with the at least one known event,and the at least one current CI time series; and classifying the atleast one current CI time series based on the re-trained projection, there-trained classifier, and the at least one current CI time series. 15.The method of claim 1, further comprising: for at least one firstsection of a first time duration of the current CI time series: for eachof the at least one known event: determining a respective second sectionof a respective second time duration of a respective representativetraining CI time series of the respective event, aligning the firstsection and the respective second section, and computing a mismatch costbetween the aligned first section and the aligned respective secondsection, applying the at least one classifier, and obtaining a tentativeclassification result based on the mismatch costs; and associating theat least one first section with at least one of: the at least one knownevent, an unknown event and the another event, based on the at least onetentative classification result.
 16. The method of claim 15, furthercomprising: computing how many times each known event achieves asmallest mismatch cost; and associating the at least one first sectionwith at least one of: a known event that achieves the smallest mismatchcost for most times, a known event that achieves a smallest overallmismatch cost, wherein an overall mismatch cost is a weighted average ofat least one mismatch cost associated with the at least one firstsection, a known event that achieves a smallest cost based on anotheroverall cost, and an unknown event, wherein the at least one firstsection is associated with the unknown event in at least one of thefollowing situations: no event achieves a mismatch cost lower than afirst threshold T1 in a sufficient percentage of the at least one firstsection, and no event achieves an overall mismatch cost lower than asecond threshold T2.
 17. The method of claim 15, wherein therepresentative training CI time series associated with the known eventis obtained based on the at least one training CI time series associatedwith the known event.
 18. The method of claim 17, wherein: therepresentative training CI time series associated with the known eventis a particular one of the at least one training CI time seriesassociated with the known event such that it has a smallest aggregatemismatch among the at least one training CI time series; the aggregatemismatch of a particular training CI time series is a function of atleast one mismatch cost between the particular training CI time seriesand each of the remaining of the at least one training CI time seriesaligned with the particular training CI time series; the functioncomprises at least one of: average, weighed average, mean, trimmed mean,median, mode, arithmetic mean, geometric mean, harmonic mean, truncatedmean, generalized mean, power mean, f-mean, interquartile mean, andanother mean.
 19. The method of claim 17, wherein: a particularrepresentative training CI time series associated with a particularknown event has a particular time duration; the particularrepresentative training CI time series of the particular time durationis aligned with each of the at least one training CI time seriesassociated with the known event and a respective mismatch cost iscomputed; the particular representative training CI time seriesminimizes an aggregate mismatch with respect to the at least onetraining CI time series; the aggregate mismatch of a CI time series is afunction of at least one mismatch cost between the CI time series andeach of the at least one training CI time series aligned with the CItime series; and the function comprises at least one of: average,weighed average, mean, trimmed mean, median, mode, arithmetic mean,geometric mean, harmonic mean, truncated mean, generalized mean, powermean, f-mean, interquartile mean, and another mean.
 20. The method ofclaim 19, wherein: the particular time duration minimizes the aggregatemismatch among a plurality of candidate time durations of the particularrepresentative training CI time series; the particular time duration andthe particular representative training CI time series with theparticular time duration are computed iteratively; a current timeduration is initialized as one of the plurality of candidate timedurations; for the current time duration, a current optimal trainedrepresentative CI time series with the current time duration iscomputed; and for the current optimal trained representative CI timeseries, the current time duration is changed to give a smallernormalized aggregate mismatch.
 21. A system having a processor, a memorycommunicatively coupled with the processor and a set of instructionsstored in the memory for recognizing an event, comprising: a firsttransmitter in a venue and configured for: for each of at least oneknown event happening in the venue in a respective training time period,transmitting a respective training wireless signal through a wirelessmultipath channel impacted by the known event in the venue in thetraining time period associated with the known event; at least one firstreceiver in the venue, wherein each of the at least one first receiveris configured for: for each of the at least one known event happening inthe venue, receiving asynchronously the respective training wirelesssignal through the wireless multipath channel, obtaining, asynchronouslybased on the respective training wireless signal, at least one timeseries of training channel information (training CI time series) of thewireless multipath channel between the first receiver and the firsttransmitter in the training time period associated with the known event,and pre-processing the at least one training CI time series; a secondtransmitter in the venue and configured for: for a current eventhappening in the venue in a current time period, transmitting a currentwireless signal through the wireless multipath channel impacted by thecurrent event in the venue in the current time period associated withthe current event; at least one second receiver in the venue, whereineach of the at least one second receiver is configured for: for thecurrent event happening in the venue in the current time period,receiving asynchronously the current wireless signal through thewireless multipath channel, obtaining, asynchronously based on thecurrent wireless signal, at least one time series of current channelinformation (current CI time series) of the wireless multipath channelbetween the second receiver and the second transmitter in the currenttime period associated with the current event, and pre-processing the atleast one current CI time series; and an event recognition engineconfigured for: training a projection for CI using a dimension reductionmethod based on the training CI time series associated with the at leastone known event, training at least one classifier for the at least oneknown event based on the at least one training CI time series and theprojection, applying the at least one classifier to: classify, based onthe projection, at least one of: the at least one current CI timeseries, a portion of a particular current CI time series, and acombination of the portion of the particular current CI time series anda portion of an additional CI time series, and associate the currentevent with at least one of: a known event, an unknown event and anotherevent, wherein a training CI time series associated with a firstreceiver and a current CI time series associated with a second receiverhave at least one of: different starting times, different timedurations, different stopping times, different counts of items in theirrespective time series, different sampling frequencies, differentsampling periods between two consecutive items in their respective timeseries, and channel information (CI) with different features.
 22. Thesystem of claim 21, wherein the event recognition engine is furtherconfigured for: aligning a first section of a first time duration of afirst CI time series and a second section of a second time duration of asecond CI time series; and determining a mapping between items of thefirst section and items of the second section.
 23. The system of claim22, wherein: the first CI time series is processed by a first operation;the second CI time series is processed by a second operation; and atleast one of the first operation and the second operation comprises atleast one of: subsampling, re-sampling, interpolation, filtering,transformation, feature extraction, and pre-processing.
 24. The systemof claim 22, wherein the event recognition engine is further configuredfor: mapping a first item of the first section to a second item of thesecond section; and applying at least one constraint on at least onefunction of at least one of: the first item of the first section of thefirst CI time series; another item of the first CI time series; a timestamp of the first item; a time difference of the first item; a timedifferential of the first item; a neighboring time stamp of the firstitem; another time stamp associated with the first item; the second itemof the second section of the second CI time series; another item of thesecond CI time series; a time stamp of the second item; a timedifference of the second item; a time differential of the second item; aneighboring time stamp of the second item; and another time stampassociated with the second item, wherein one of the at least oneconstraint is that a difference between the time stamp of the first itemand the time stamp of the second item is upper-bounded by an adaptiveupper threshold and lower-bounded by an adaptive lower threshold. 25.The system of claim 21, wherein the event recognition engine is furtherconfigured for: determining a section of a time duration of a CI timeseries adaptively; and determining a starting time and an ending time ofthe section, wherein determining the section comprises: computing atentative section of the CI time series, and determining the section byremoving a beginning portion and an ending portion of the tentativesection.
 26. The system of claim 21, wherein: at least one of: thecurrent event and the at least one known event is related to security;the event recognition engine is coupled to at least one of: the firsttransmitter, the second transmitter, an additional transmitter, the atleast one first receiver, the at least one second receiver, anadditional receiver, a cloud server, a fog server, a local server, andan edge server; the first transmitter and the second transmitter are ata same location in the venue; and at least one of: a subset of the atleast one first receiver is a permutation of a subset of the at leastone second receiver.
 27. An event recognition engine of a wirelessmonitoring system, comprising: a processor; a memory communicativelycoupled with the processor; and a set of instructions stored in thememory which, when executed, causes the processor to perform: for eachof at least one known event happening in a venue in a respectivetraining time period, obtaining, from each of at least one firstreceiver in the venue, at least one time series of training channelinformation (training CI time series) of a wireless multipath channelimpacted by the known event, wherein the first receiver extracts thetraining CI time series from a respective training wireless signalreceived from a first transmitter in the venue through the wirelessmultipath channel between the first receiver and the first transmitterin the training time period associated with the known event, training aprojection for CI using a dimension reduction method based on thetraining CI time series associated with the at least one known event,training at least one classifier for the at least one known event basedon the at least one training CI time series and the projection, for acurrent event happening in the venue in a current time period:obtaining, from each of at least one second receiver in the venue, atleast one time series of current channel information (current CI timeseries) of the wireless multipath channel impacted by the current event,wherein the second receiver extracts the current CI time series from acurrent wireless signal received from a second transmitter in the venuethrough the wireless multipath channel between the second receiver andthe second transmitter in the current time period associated with thecurrent event, applying the at least one classifier to: classify, basedon the projection, at least one of: the at least one current CI timeseries, a portion of a particular current CI time series, and acombination of the portion of the particular current CI time series anda portion of an additional CI time series, and associate the currentevent with at least one of: a known event, an unknown event and anotherevent, wherein a training CI time series associated with a firstreceiver and a current CI time series associated with a second receiverhave at least one of: different starting times, different timedurations, different stopping times, different counts of items in theirrespective time series, different sampling frequencies, differentsampling periods between two consecutive items in their respective timeseries, and channel information (CI) with different features.
 28. Theevent recognition engine of claim 27, wherein the set of instructionsstored in the memory, when executed, further causes the processor toperform: aligning a first section of a first time duration of a first CItime series and a second section of a second time duration of a secondCI time series; and determining a mapping between items of the firstsection and items of the second section, wherein: the first CI timeseries is processed by a first operation, the second CI time series isprocessed by a second operation, and at least one of the first operationand the second operation comprises at least one of: subsampling,re-sampling, interpolation, filtering, transformation, featureextraction, and pre-processing.
 29. The event recognition engine ofclaim 27, wherein the set of instructions stored in the memory, whenexecuted, further causes the processor to perform: determining a sectionof a time duration of a CI time series adaptively; and determining astarting time and an ending time of the section, wherein determining thesection comprises: computing a tentative section of the CI time series,and determining the section by removing a beginning portion and anending portion of the tentative section.
 30. A receiver of a wirelessmonitoring system, comprising: a wireless circuitry configured to: foreach of at least one known event happening in a venue in a respectivetraining time period, receive a respective training wireless signalthrough a wireless multipath channel impacted by the known event,wherein the respective training wireless signal is transmitted by afirst transmitter through the wireless multipath channel between thereceiver and the first transmitter in the training time periodassociated with the known event, and for a current event happening inthe venue in a current time period, receive a current wireless signalthrough the wireless multipath channel impacted by the current event,wherein the current wireless signal is transmitted by a secondtransmitter through the wireless multipath channel between the receiverand the second transmitter in the current time period associated withthe current event; a processor communicatively coupled with the wirelesscircuitry; a memory communicatively coupled with the processor; and aset of instructions stored in the memory which, when executed, causesthe processor to: for each of the at least one known event happening inthe venue, obtain, asynchronously based on the respective trainingwireless signal, at least one time series of training channelinformation (training CI time series) of the wireless multipath channel,and for the current event happening in the venue in the current timeperiod, obtain, asynchronously based on the current wireless signal, atleast one time series of current channel information (current CI timeseries) of the wireless multipath channel, wherein: a projection for theCI is trained using a dimension reduction method based on the trainingCI time series associated with the at least one known event, the atleast one training CI time series is used by an event recognition engineof the wireless monitoring system to train at least one classifier forthe at least one known event based on the projection, and the at leastone classifier is applied to: classify, based on the projection, atleast one of: the at least one current CI time series, a portion of aparticular current CI time series, and a combination of the portion ofthe particular current CI time series and a portion of an additional CItime series, and associate the current event with at least one of: aknown event, an unknown event and another event.